Generative AI Showcase

Gen AI Success Stories

Manufacturing
Healthcare
BFSI
Supply Chain Management
Misc.

AI for Brand Logo Migration Post-Acquisition

Gen AI helped a manufacturing company update their brand logo across 2 M+ documents spanning 15 different document management systems. Using AI, our client was able to migrate logos in 3 weeks instead of the projected 8 months.

Gen AI helped a manufacturing company update their brand logo across 2 M+ documents spanning 15 different document management systems. Using AI, our client was able to migrate logos in 3 weeks instead of the projected 8 months.

Problem Statement

Our client, who recently acquired another company, faced brand consistency challenges post-acquisition. They had more than 2 million documents requiring logo updates. Documents were stored in different systems across geographies and had multiple file formats: PDF (45%), Word (28%), PowerPoint (15%), Excel (8%), and others (4%).

Our Solution

Our AI solution used computer vision to help the client migrate logos across its company documents.

Document Discovery and Classification – Deployed AI scanning tools to identify and categorize documents containing the old logo, creating an intelligent classification system that considered document type, logo placement, regulatory requirements, and business criticality.
AI Model Development – Custom computer vision model helped detect logo variations, assess quality, and determine positioning. We also implemented automated quality assurance protocols and parallel processing capabilities.
Automated Update Process – We implemented a batch processing handling 100,000 documents daily with an automated workflow for logo detection, quality assessment, replacement execution, and version control. ## Results

Business Benefits

Our client was able to maintain brand consistency across all its documents and collaterals leading to:

Processing 2 M+ documents in 3 weeks
99 % accuracy rate in logo replacement
$2.5 million in cost savings from this initiative

We have packaged the AI-driven reusable framework for M&A branding updates aimed at helping enterprises with brand standardization across company documents.

Workforce Training

A renowned global leader in innovative bicycle design and high-quality manufacturing, offering a diverse range of bikes for casual riders, adventurers, and professional athletes.

A global leader in bicycle design and manufacturing, offering a diverse range of bikes for casual riders, adventurers, and professional athletes.

Problem Statement

The company faced challenges in managing their RFP process, including manual content creation, tedious data entry, and time-consuming review processes. They needed a solution that could automate the RFP process and improve efficiency.

Our Solution

We developed a comprehensive solution that leveraged Gen AI to automate the RFP process. The solution consisted of:

Automated Content Generation: Gen AI generated RFP content, including responses to specific requirements and questions, saving time and effort.
Natural Language Processing (NLP): NLP capabilities allowed Gen AI to understand and interpret natural language, enabling it to extract relevant information from RFP documents and generate contextually appropriate responses.
Customization and Personalization: Gen AI customized and personalized RFP responses based on the unique characteristics and preferences of the soliciting organization.
Data Analytics and Insights: Gen AI leveraged data analytics and machine learning algorithms to analyze past RFPs, historical data, and performance metrics, providing valuable insights for decision-making.
Scalability: Gen AI was highly scalable, allowing the company to handle a large volume of RFPs and responses efficiently.

Business Benefits

Improved Efficiency: The automated RFP process improved efficiency by 60%, reducing the time and effort required for manual content creation and data entry.
Enhanced Accuracy: Gen AI ensured accurate and consistent responses, reducing errors and improving the overall quality of the RFP process.
Increased Productivity: The automated RFP process increased productivity by 25%, allowing the company to focus on higher-value activities and strategic initiatives.
Cost Savings: The automated RFP process resulted in significant cost savings, reducing the need for manual labor and improving the overall cost-effectiveness of the process.
Improved Win Rates: Gen AI-driven insights helped with go/no-go decision-making and tailored proposals to better meet the criteria, significantly improving the chances of winning bids.
Faster Response Time: Project management and collaborative tools helped move teams along, and immediate access to up-to-date and approved information enabled project managers to fine-tune responses instead of searching for answers.

Synthetic Data for AVs

A prominent US-based technology leader making AI-powered chips for automobiles and electric vehicles, as well as manufacturing home and grid-scale battery storage systems.

Our customer is a leading US-based tech company that makes AI-based chips for automobiles and also designs chips for electric vehicles, as well as manufactures home and grid-scale battery energy storage and related equipment.

Problem Statement

The company faced challenges in training their AV perception algorithms on real-world data alone. Real-world data can be limited, biased, and expensive to collect, especially for rare or dangerous scenarios. They needed a solution that could supplement their real-world data with high-quality synthetic data to improve the robustness and accuracy of their perception systems.

Our Solution

Our solution used photorealistic 3D simulation environments to generate synthetic driving scenarios, including rare and edge cases that would be difficult or dangerous to capture in the real world.
We employed novel view synthesis techniques to generate synthetic data from different sensor viewpoints, mimicking the diverse sensor configurations found across different vehicle types.
Automatically generated ground truth annotations, such as 3D bounding boxes and object classifications, to enable efficient training of AV perception models.
Validated the synthetic data for realism and diversity, and seamlessly integrated it into the customer’s AV perception training pipeline.

Business Benefits

The synthetic data helped improve the accuracy and robustness of the customer’s AV perception models, particularly in handling rare and edge cases.
The automated data generation and annotation capabilities significantly reduced the time and cost required to collect and prepare training data.
The improved perception performance enabled by synthetic data enhanced the overall safety of the customer’s AV systems, reducing the risk of accidents.
The ability to generate diverse and customized synthetic data allowed the customer to efficiently explore and validate their AV perception algorithms across a wide range of scenarios.

Product Development

A prominent global industrial technology company and leading manufacturer of advanced CNC machine tools, with 12 wholly-owned subsidiaries worldwide and over 35 years of industry expertise.

A global industrial technology company and a leading manufacturer of CNC machine tools. Our client has 12 wholly-owned subsidiaries worldwide and is listed on NASDAQ. With over 35 years of experience, our client has a wide range of machine tools.

Problem Statement

The company’s existing product development approach was rigid, siloed, and overly focused on internal capabilities rather than market demands. New product launches were often delayed, and the resulting offerings failed to resonate with customers. The leadership recognized the need for a more agile, customer-centric product development strategy.

Our Solution

Generative AI (GenAI): The company employed GenAI to generate product concepts, designs, and marketing content based on customer insights and market trends. This accelerated the ideation process and ensured alignment with customer preferences.
Retrieval Augmented Generation (RAG): RAG was used to augment the company’s product data with relevant external information, such as industry benchmarks, competitor analysis, and market forecasts. This provided the product development teams with a more comprehensive view of the market landscape
PLM software usage: The manufacturer leveraged advanced digital tools and analytics to streamline the entire product lifecycle, from ideation to launch. This included product lifecycle management (PLM) software, simulation tools, and data-driven decision-making.

Business Benefits

Reduced Time-to-Market: The company was able to cut the average product development cycle time by 28%, enabling them to respond more quickly to market changes.
Improved Customer Satisfaction: Customer satisfaction scores increased by 22% as the new products better met their needs and expectations.
Enhanced Innovation: The company’s pipeline of innovative products grew by 35%, as the agile, customer-centric approach fostered a culture of creativity and risk-taking.
Operational Efficiency: The digital tools and cross-functional collaboration led to a 19% reduction in product development costs and a 15% improvement in manufacturing productivity.

Production Planning and Inventory Management

A global industrial technology leader providing expertise in electrification, automation, and digitization for smart industries, infrastructure, data centers, buildings, and homes.

Guided Machine Maintenance

A publicly listed company that produces and exports a wide range of two and three-wheeler vehicles, recognized globally as the best manufacturer in this category.

A listed company producing and exporting two and three wheeler. Our customer is also awarded as world’s best two and three wheeler manufacturing company.

Problem Statement

The company faced challenges in maintaining their machines, which resulted in frequent breakdowns and unplanned downtime. They needed a solution that could guide maintenance personnel through the maintenance process, ensuring that machines were properly maintained and reducing downtime.

Our Solution

Collected data on the company’s machines, including their performance, usage, and maintenance history.
Used Gen AI-powered maintenance guidance to analyze the collected data and provide maintenance personnel with step-by-step instructions on how to perform maintenance tasks.
Maintenance Task Management: Managed maintenance tasks, including scheduling, assigning, and tracking maintenance activities.
Real-Time Monitoring: Monitored machine performance in real-time, providing maintenance personnel with real-time data on machine health and performance.

Business Benefits

Decreased workplace accidents related to equipment failures by around 35%.
Our solution improved provided maintenance personnel with step-by-step instructions on how to perform maintenance tasks.
This reduced downtime by ensuring that machines were properly maintained and reducing the likelihood of breakdowns.
Reduced operating costs in the long run by preventing major equipment issues and the need for costly repairs.
Real-time data on machine health and performance was made available for maintenance personnel, allowing them to make informed decisions about maintenance activities.

Product Quality Intelligence

An American manufacturing company founded in 1993, headquartered in Boston, working in aerospace, energy, and pioneering transformative technologies with 6,000+ employees.

An American multinational manufacturing company founded in 1963 headquartered in Boston. The company works in several divisions, including aerospace and energy. Our Client has pioneered technologies that transformed industries and improved millions of lives. The firm has 6000+ employees.

Problem Statement

The company’s existing quality control processes relied heavily on manual inspections and sampling, which were time-consuming, subjective, and prone to errors. With increasing product complexity and production volumes, the traditional approach was no longer scalable or effective. The manufacturer recognized the need for a more data-driven, intelligent quality management system.

Our Solution

We implemented an AI-powered product quality intelligence platform with the following key components:

Computer Vision and Image Analysis: High-resolution cameras were installed at critical points in the production line to capture images of assembled products. Advanced computer vision algorithms were used to detect defects, measure key dimensions, and identify anomalies with a high degree of accuracy.
Sensor Data Analytics: Sensor data from various stages of the manufacturing process, such as temperature, pressure, and vibration, were analyzed in real-time using machine learning models. This enabled the early detection of process deviations that could lead to quality issues.
Predictive Maintenance: The AI platform leveraged historical data on equipment performance, maintenance logs, and sensor readings to predict when critical machinery was likely to fail. This allowed the company to proactively schedule maintenance and avoid unplanned downtime.
Closed-Loop Quality Control: The insights generated by the AI platform were fed back into the manufacturing process, enabling automated adjustments to process parameters, tool settings, and material inputs to maintain optimal quality.
Augmented Reality (AR) Quality Inspection: AR-enabled tablets were provided to quality inspectors, allowing them to overlay computer vision-based defect detection results onto physical products. This enhanced the inspectors’ ability to identify and classify defects accurately.

Business Benefits

Defect Reduction: The AI-powered quality control system reduced the incidence of defects by 28%, leading to significant cost savings and improved customer satisfaction.
Increased Productivity: By automating repetitive inspection tasks and providing real-time process insights, the platform enabled a 15% increase in production throughput.
Improved Yield: The closed-loop quality control and predictive maintenance capabilities resulted in a 12% improvement in manufacturing yield.
Enhanced Traceability: The system provided comprehensive data on product quality, process performance, and equipment health, enabling better traceability and root cause analysis.

Industrial Instrument Longevity

A leading provider of industrial automation and digital transformation solutions, building systems integrating components like PLCs, HMIs and sensors for real-time monitoring and control of industrial processes.

Our client is a leading provider of industrial automation and digital transformation solutions. The company builds automation and control systems which integrate various industrial components, such as programmable logic controllers, human-machine interfaces and sensors, which enable real-time monitoring and control of industrial processes.

Problem Statement

The company faced challenges in maintaining their industrial instruments, including the risk of obsolescence and the need for frequent replacements. They needed a solution that could help them extend the life of their instruments and reduce the risk of obsolescence.

Our Solution

Used Gen AI to continuously monitor the performance and condition of industrial instruments, identifying potential issues before they become critical.
Predictive Maintenance: Employed Gen AI algorithms to predict when maintenance or replacement of instruments would be required, enabling proactive and cost-effective interventions.
Obsolescence Management: Utilized Gen AI to analyze market trends, technological advancements, and end-of-life notifications to anticipate and mitigate the risk of instrument obsolescence.
Calibration Optimization: Applied Gen AI to optimize the calibration schedules of instruments, ensuring accurate readings while minimizing unnecessary downtime.

Business Benefits

Our solution extended the average lifespan of industrial instruments by 25%, reducing the need for frequent replacements.
The predictive maintenance capabilities of the Gen AI solution reduced maintenance costs by 18% by addressing issues before they escalated.
Our solution improved overall operational efficiency by 15% by minimizing unplanned downtime and optimizing calibration schedules.
The obsolescence management capabilities of the Gen AI solution reduced the risk of instrument obsolescence by 30%, ensuring long-term viability of the company’s assets.

AI Image Generation

A well-known leader in the agricultural manufacturing industry, producing a comprehensive range of essential farm equipment including tractors, harvesters, and other advanced machinery.

A well-known company in the agriculture manufacturing space which Tractors, Harvesters and other farm equipments.

Problem Statement

The company faced challenges in generating high-quality images of their products for inspection and quality control purposes. They needed a solution that could generate realistic and accurate images of their products, reducing the need for physical prototypes and improving the efficiency of their production process.

Our Solution

Collected data on the company’s products, including their design, materials, and manufacturing processes.
Trained an AI model using the collected data to generate high-quality images of the products.
Image Generation: Used the trained AI model to generate images of the products, including their design, materials, and manufacturing processes.
Enhanced the generated images to improve their quality and accuracy.

Business Benefits

The AI image generation solution improved product quality by reducing the need for physical prototypes and improving the accuracy of product inspections.
Production costs was reduced by reducing the need for physical prototypes and improving the efficiency of the production process.
Efficiency was improved by reducing the time and cost associated with generating high-quality images of the products.
The solution enhanced decision-making by providing high-quality images of the products, allowing for more accurate and informed decisions.

Talk to your Data

A major manufacturer of sports equipment and the world’s largest supplier of athletic shoes, apparel, and other high-performance sporting goods for professional and recreational athletes.

A major manufacturer of sports equipment and world’s largest supplier of athletic shoes and apparel. The company also deals in athletic footwear and apparels.

Problem Statement

The company faced challenges in analyzing large volumes of data from various sources, including sensors, machines, and customer feedback. They needed a solution that could integrate and analyze this data to provide actionable insights and improve decision-making processes.

Our Solution

Used Gen AI to integrate data from various sources, including sensors, machines, and customer feedback.
With the help of RAG we analyzed the integrated data and identify patterns, trends, and correlations.
Using Gen AI we generated actionable insights from the analyzed data, including predictive maintenance, quality control, and supply chain optimization.
Finally, we used RAG to provide decision support by generating recommendations based on the insights generated by Gen AI.

Business Benefits

Improved Data Analysis: The solution improved data analysis by integrating and analyzing large volumes of data from various sources.
Enhanced Decision-Making: The solution enhanced decision-making by providing actionable insights and recommendations based on the analyzed data.
Increased Efficiency: The solution increased efficiency by automating data analysis and decision-making processes.
Improved Customer Satisfaction: The solution improved customer satisfaction by providing personalized recommendations and improving product quality.

Boosting Staff Efficiency and Patient Satisfaction with Gen AI

We applied our expertise to develop a Gen AI-powered chatbot for patient access: scheduling, billing inquiries, personalized medical guidance, and virtual assistant integration

Our client is a multispecialty healthcare org with over 5000 beds and 40+ departments widespread across the USA, they sought to expand their services and patient base. However, their current operational structure hindered efficient growth.

Problem Statement

The firm struggles with several challenges hindering growth and optimal patient care: Difficulty accessing information (appointments, billing) for patients Overburdened staff answering repetitive questions

Our Solution

We applied our expertise in to develop a comprehensive Gen AI-powered solution:

Multi-Functional Chatbot: Built using Natural Language Processing (NLP), offered patients 24/7 access to:
Appointment scheduling and confirmation
Billing inquiries and insurance verification
Personalized medical information and appointment preparation guidance
Pre-screening questionnaires and symptom checkers
Seamless Virtual Assistant Integration: Chatbot seamlessly integrated with a virtual assistant powered by RAG algorithms and enabled features like:
Real-time connection with live agents for complex inquiries
Secure appointment scheduling directly within the chatbot interface
Integration with hospital scheduling systems for automated appointment booking

Business Benefits

30% Staff Efficiency Improved by reducing call volume and repetitive inquiries.
47% Enquires were resolved by the chatbot itself
The Gen AI-powered solution can handle a high volume of inquiries without additional resources, leading to scalability and growth.

Healthcare Administrative Assistant

We implemented automation with RPA and Gen AI for administrative tasks, enhancing career paths with personalized coaching consultations.

A not-for-profit Catholic health care system operating multiple hospitals and medical clinics across seven states, of US. It provides a comprehensive range of health and social services, including outpatient care, transitional care, home and hospice care, substance abuse programs, mental health treatment, prevention and wellness programs, long-term care, and assisted living and housing.

Problem Statement

The healthcare network’s administrative assistants were often overburdened with manual, repetitive tasks that differed from their ability to provide high-quality support to clinicians and patients. This led to frustration, burnout, and suboptimal patient experiences.

Our Solution

We implemented an automation and upskilling program for their administrative assistants:

Intelligent Process Automation: They deployed robotic process automation (RPA) and Gen AI powered tools to automate routine administrative tasks such as appointment scheduling, patient registration, and medical coding. This freed up the assistants to focus on higher-value work.
Natural Career Advancement Opportunities : By expanding the scope of the administrative assistant role, we created new pathways for career growth and professional development by one on one consultation with the help of our AI generated consulting coach.

Business Benefits

Improved Efficiency: Automation of repetitive tasks led to a 25% reduction in administrative workload, allowing assistants to be more responsive to clinician and patient needs.
Enhanced Job Satisfaction: 72% of administrative assistants reported higher job satisfaction due to the opportunity to take on more meaningful work.
Better Patient Experiences: With administrative assistants freed up to provide personalized support, patient satisfaction scores increased by 18%.

Automating Patient Care Navigation

We implemented a GenAI and RAG-powered platform for capturing symptoms, triaging patients, offering personalized care plans, and automating clinical documentation.

A teaching hospital located in North America Founded in 1834 having a rich history. The hospital played a significant role in medical education and clinical training. With 945 beds, it provides a wide range of services, including an emergency department.

Problem Statement

The healthcare provider’s existing patient care navigation process was inefficient and burdensome for both patients and staff. Patients often faced long wait times, confusion about where to go for care, and a bad user experience. Meanwhile, clinical staff were overwhelmed with administrative tasks related to patient intake, triage, and routing.

Our Solution

We implemented a GenAI and RAG-powered digital navigation platform that could:

Capture patient symptoms and other relevant data through conversational AI.
Intelligently triage patients and route them to the right level of care, whether virtual, in-person, or to the emergency department.
Provide patients with personalized care plans, educational resources, and appointment scheduling.
Automate clinical documentation using RAG to save time for providers.

Business Benefits

17% of patients were served through automated self-service features, reducing administrative workload.
Patient engagement rates increased by 58% as the digital platform provided a more seamless and personalized experience.
Hospital readmission rates decreased by 14% as patients were better supported before and after visits.
97% of patients rated their experience 4/5 or higher, indicating high satisfaction.

Autogenerating Clinical Documentation

Our solution integrates advanced NLP for accurate clinical documentation, multimodal data integration, personalized templates, real-time assistance, and continuous learning from physician feedback.

Our Client, a 2,000+ bed acute-care medical facility in the USA provides exceptional medical care, including world-class cancer care, specialized pediatric care and an advanced wound care. It is nationally ranked in 4 adult and 6 pediatric specialty and rated high performing in 6 adult specialties and 15 procedures and conditions.

Problem Statement

Physicians at the healthcare system were spending an inordinate amount of time on documentation, often having to work late into the evenings to complete their notes. This administrative burden was contributing to physician burnout and negatively impacting their ability to focus on patient care. The existing documentation process was also prone to inconsistencies and errors, which raised concerns around regulatory compliance and patient safety.

Our Solution

The key components of the solution we provide consisted of:

Natural Language Processing (NLP): Advanced NLP models were trained on the organization’s historical clinical notes and medical knowledge to understand the structure, terminology, and best practices for documentation.
Multimodal Data Integration: The platform ingested data from various sources, including electronic health records, diagnostic imaging, lab results, and physician-patient conversations, to generate comprehensive and accurate clinical notes.
Personalized Templates:The AI system leveraged machine learning to create personalized note templates for each physician, reflecting their unique documentation styles and preferences.
Real-Time Assistance: As physicians conducted patient encounters, the auto generation platform provided real-time suggestions and auto-completed sections of the clinical note, reducing the manual effort required.
Continuous Learning:The system continuously learned from the physicians’ feedback and edits to the autogenerated notes, improving the quality and relevance of the output over time.

Business Benefits

Reduced Documentation Burden: Physicians experienced a 35% reduction in the time spent on clinical documentation, allowing them to focus more on patient care.
Improved Note Quality: The autogenerated notes were found to be more consistent, comprehensive, and compliant with regulatory standards, as compared to manually written notes.
Enhanced Physician Satisfaction: Surveys indicated a 28% increase in physician satisfaction due to the reduced administrative workload and improved work-life balance.
Increased Coding Accuracy: The detailed and structured clinical notes enabled more accurate medical coding, leading to a 12% increase in reimbursement revenue.

Member EOB and Provider Bill Matching Tool

We developed a tool integrating member EOB and provider bill data, employing advanced algorithms for accurate matching and real-time updates.

A leading health insurance company, aiming to improve the accuracy and efficiency of member EOBs and provider bills through the development of a matching tool.

Problem Statement

The company faced challenges in ensuring that member EOBs and provider bills accurately matched, leading to confusion and potential errors. They needed a solution that could streamline the process and reduce the risk of discrepancies.

Our Solution

We developed a comprehensive solution that integrated a member EOB and provider bill matching tool. The tool consisted of:

Data Integration: Integrated member EOB and provider bill data to ensure accurate matching.
Algorithmic Matching: Employed advanced algorithms to match EOBs and bills based on various criteria, including member ID, provider information, and service dates.
Real-time Updates: Provided real-time updates to ensure that both EOBs and bills were accurate and up-to-date.
Error Detection: Detected and flagged potential errors or discrepancies between EOBs and bills, enabling prompt resolution.

Business Benefits

The matching tool improved the accuracy of EOBs and bills, reducing the overall risk of errors and discrepancies by 27%.
The tool streamlined the process, reducing the time and effort required to match EOBs and bills.
Enhanced Transparency: Provided real-time updates and detailed information, enhancing transparency and reducing confusion.
Reduced Disputes: The tool reduced disputes between members and providers, improving overall satisfaction and reducing administrative burdens

Personalized Patient Communication

We have developed our solution by integrating patient data, training a Gen AI model for personalized messages, and delivering them through email, text, and patient portals.

EHR Search and Summarization

Developed AI-powered EHR search & summarization: NLP for document search, deep learning models for info extraction, RAG for concise summaries.

AI powered Summarization for Research Publications

We developed an AI-powered Enhanced Medical Search solution using a hybrid approach that combines Gen AI and Natural Language Processing (NLP)

A leading medical research institution with a vast collection of medical literature, ranked amongst top 100 research institutes in United States by QS world rankings.

Problem Statement

The institution faced challenges in efficiently searching for relevant medical literature, with researchers spending 30% of their time on manual searches and filtering through large volumes of data. This process was time-consuming and often led to missed opportunities for breakthroughs.

Our Solution

We developed an AI-powered Enhanced Medical Search solution using a hybrid approach that combines Gen AI and Natural Language Processing (NLP). This solution consists of:

Semantic Search for documents: Analyzes medical literature from various sources, including PubMed, Scopus, and Web of Science.
Natural Language Processing (NLP) for Text Analysis: Analyzes the retrieved literature using NLP techniques.
Generative AI for Summary Generation: Generates concise and informative summaries highlighting key findings.
Personalized Recommendations: Learns from researchers’ interactions and feedback to provide personalized recommendations.

Business Benefits

Reduced Search Time: Researchers spent 30% less time searching for relevant articles, allowing them to focus on high-value tasks.
Improved Search Accuracy: The solution identified relevant articles 25% more accurately compared to traditional methods.
Increased Research Productivity: The solution led to a 20% increase in research productivity, with researchers able to publish more papers and make breakthrough discoveries.
Cost Savings: The solution resulted in cost savings of $300,000 per year. The Cost Savings are also due to reduced search time, improved research productivity, and enhanced collaboration.

Embedded Banking Product Pricing and Testing

We devised a pricing and testing strategy for an embedded banking product using Gen AI for market analysis, feedback collection, compliance, and pricing scripts.

A company operating in online payment systems. Providing an electronic alternative to traditional checks and money orders, helping online vendors and commercial users by supporting online money transfers in exchange for a fee. Our customer was aiming to develop an embedded banking product that offers financial services to its customers.

Problem Statement

The company faced challenges in pricing and testing its embedded banking product, which required a comprehensive understanding of the market, customer needs, and regulatory requirements. They needed a solution that could automate the pricing and testing process while ensuring accuracy and efficiency.

Our Solution

We developed a pricing and testing strategy for the embedded banking product to analyze market data, customer feedback, and regulatory requirements. The solution:

Used Gen AI to analyze market data and identify pricing trends, customer needs, and competitive offerings.
Collected customer feedback using RAG, which combines language models with information retrieval to provide more accurate and relevant responses.
Ensured compliance with regulatory requirements, such as anti-money laundering (AML) and know-your-customer (KYC) regulations, using Gen AI to analyze and interpret regulatory guidelines.
Developed a pricing strategy using Gen AI to generate thousands of pricing scripts tailored to individual customers, balancing revenue goals with customer needs and market conditions.
Designed a testing strategy using RAG to simulate customer interactions and identify potential issues or bugs in the product.

Business Benefits

The Gen AI and RAG-powered pricing and testing strategy improved pricing accuracy by 25%, ensuring that the product was competitively priced and met customer needs.
The automated pricing and testing process reduced the time-to-market by 30%, allowing the company to launch the product faster and gain a competitive advantage.
The Gen AI-powered regulatory compliance ensured that the product met all relevant regulations, reducing the risk of non-compliance and associated penalties.
The RAG-powered testing strategy identified potential issues and bugs in early development process, improving product quality and reducing the need for costly post-launch fixes.

Customer Service Copilot

Our AI-Powered Customer Service Chatbot uses NLP for queries, ML for insights, automates BFSI workflows, and learns from interactions.

Our customer serves individuals, small- and middle-market businesses, large corporations, and governments with a full range of banking, investment management, and other financial and risk management products and services. Their commitment to responsible growth is resolute, focusing on client needs, sustainable growth, and operational excellence.

Problem Statement

The bank faced challenges in providing personalized and efficient services to its customers, with frontline employees spending significant time on manual tasks and information gathering. This led to delays in customer response times and suboptimal service quality.

Our Solution

We developed a new AI-powered solution, called “”AI-Powered Customer Service Chatbot””, which uses Natural Language Processing (NLP) and Machine Learning (ML) to assist frontline employees in the BFSI industry. This solution consists of:

NLP for Customer Understanding: Analyzes customer queries and requests to understand their needs and provide accurate responses.
ML for Predictive Insights: Analyzes customer data and internal knowledge bases to provide frontline employees with relevant information and insights.
AI-powered Workflow Automation: Automates repetitive tasks and workflows, such as account opening, loan processing, and claims management.
Continuous Learning and Improvement: Learns from employee feedback and customer interactions to improve the accuracy and relevance of recommendations.

Business Benefits

Reduced Operational Costs: The AI Copilot resulted in cost savings of $250,000 per year through process automation and improved efficiency.
Increased Revenue: The AI Copilot contributed to a 10% increase in revenue through improved cross-selling and upselling opportunities.
Enhanced Compliance: The AI Copilot improved compliance by 20% through automated analysis and interpretation of regulatory guidelines.
Improved Data Insights: The AI Copilot improved data insights by 25% through simplified generation of insights and predictions based on operational data.

Banking Product Recommendation Engine

We developed a banking recommendation engine using Gen AI and machine learning to analyze customer data for personalized product suggestions across channels.

A multinational investment bank and financial services company headquartered in New York City, operates through multiple subsidiaries in more than 17 countries with more than 10,000 employees. It operates in global markets with capital origination, corporate and investment banking, markets and securities services, private bank, and treasury and trade solutions, as well as in retail banking.

Problem Statement

The company faced challenges in providing personalized banking product recommendations to its customers, leading to low conversion rates and high customer churn.

Our Solution

We developed a banking product recommendation engine using Gen AI-powered recommendation algorithms and machine learning models to analyze customer data and preferences. This solution consisted of:

Gen AI-powered Recommendation Algorithms: We Analyzed customer data and preferences to identify patterns and trends.
Generated personalized product recommendations based on customer interests and behaviors.
Machine Learning Models: We Used machine learning models to predict customer behavior and preferences.
Adapted product recommendations based on customer interactions and feedback.
Product Recommendation: We Created personalized product recommendations for each customer based on their interests and preferences.
Used various channels such as online banking, mobile apps, and branch visits to deliver personalized product recommendations.

Business Benefits

Improved Customer Engagement: The banking product recommendation engine improved customer engagement by 25% through personalized and relevant product recommendations.
Increased Conversion Rates: The solution increased conversion rates by 8% through targeted and personalized product recommendations.

Customer Churn Prediction

We created a customer churn prevention solution with machine learning, data analytics, and digital experience, featuring segmentation, predictive modeling, personalized offers, and improved digital interactions.

A leading bank that serves a diverse customer base, including individuals, small businesses, and large corporations. The bank has a strong focus on building long-term relationships with its customers and providing them with the tools and resources they need to succeed.

Problem Statement

We developed a comprehensive customer churn prevention solution using a combination of machine learning algorithms, data analytics, and digital customer experience. The solution consisted of:

Our Solution

The bank faced high customer churn rates, with 20% of customers leaving within the first year. The main reasons for churn were poor customer service, lack of personalized offers, and inadequate product offerings. The bank needed a solution to identify at-risk customers and prevent churn before it happened.

Semantic Search for documents: Customer Segmentation: We segmented customers based on their behavior, demographics, and transactional data to identify high-risk customers.
Predictive Modeling: We developed predictive models using machine learning algorithms to identify customers who were likely to churn.
Personalized Offers: We created personalized offers and promotions for high-risk customers to retain them.
Digital Customer Experience: We improved the digital customer experience by providing easy access to account information, streamlined transactions, and proactive communication.

Business Benefits

The solution resulted in a significant reduction in customer churn rates, with a 30% decrease in the first year.
Improved Customer Retention: The solution improved customer retention rates by 25%.
Reduced Churn Costs: The solution reduced churn costs by 40%.
Improved Revenue: The solution improved revenue by 15%.

Synthetic Credit Data (POC)

We developed a synthetic credit data solution using machine learning and Gen AI for data augmentation, bias mitigation, and model validation.

An American multinational financial services company with a significant global presence. The company operates in 19 countries and serves over 5 million customers worldwide. It is a systemically important financial institution according to the Financial Stability Board.

Problem Statement

The bank faced challenges in creating a fair and inclusive credit scoring system, as traditional credit scoring models often relied on biased data and were prone to errors. The bank needed a solution that could generate synthetic credit data to mitigate these biases and improve the accuracy of credit decisions.

Our Solution

We developed a synthetic credit data solution that used machine learning and Gen AI algorithms to generate synthetic credit data that was similar to real-world credit data. It consisted of:

Data Augmentation: We used data augmentation techniques to expand the existing credit data and reduce the risk of overfitting.
Bias Mitigation: We used bias mitigation techniques with the help of Gen AI to ensure that the synthetic credit data was free from biases and errors.
Model Validation: We used model validation techniques to ensure that the synthetic credit data was accurate and reliable.

Potential Business Benefits

Improved Credit Scoring: The synthetic credit data solution improved credit scoring by reducing bias and improving the accuracy of credit decisions.
Increased Customer Trust: The solution increased customer trust by providing a fair and inclusive credit scoring system.
Enhanced Compliance: The solution enhanced compliance with regulatory requirements by providing a robust and transparent credit scoring system.
Increased Efficiency: The solution increased efficiency by reducing the need for manual data processing and improving the speed of credit decisions.

Banking Fraud Prevention

We developed a fraud prevention solution with advanced analytics, machine learning, and Gen AI to detect and prevent fraudulent activities in real-time.

A leading Bank with a large customer base and a complex financial ecosystem. Our client prioritizes robust security measures to protect its customers from financial fraud.

Problem Statement

The bank faced significant challenges in preventing fraud, including the increasing sophistication of fraudsters and the growing volume of transactions. The bank needed a comprehensive fraud prevention solution that could detect and prevent fraudulent activities in real-time.

Our Solution

We developed a comprehensive fraud prevention solution that integrated advanced analytics, machine learning, and Gen AI to detect and prevent fraudulent activities. The solution consisted of:

Advanced Analytics: Analyzed large datasets to identify patterns and trends that could indicate fraudulent activity.
Machine Learning: Used machine learning algorithms to identify previous fraud patterns and predict future fraudulent activities.
Generative AI: Used Generative AI-powered fraud detection to analyze data in real-time and detect fraudulent activities.
Fraud Prevention: Implemented fraud prevention techniques such as blocking suspicious transactions and alerting customers to potential fraud.

Business Benefits

Reduced Fraud Losses: The fraud prevention solution reduced fraud losses by 80% through effective detection and prevention of fraudulent activities.
Improved Customer Trust: The solution improved customer trust by providing a secure and reliable banking experience.
Enhanced Compliance: The solution enhanced compliance with regulatory requirements by providing a robust fraud prevention framework.
Increased Efficiency: The solution increased efficiency by automating fraud detection and prevention processes, reducing manual intervention and improving response times.

Generation of financial reports and loan agreements

We automated document tasks with AI for data extraction, validation, classification, and management, enhancing accuracy, efficiency, and data security.

Our customer is one of India’s largest retail Non-Banking Financial Company (NBFC). The firm has a network of 1,705 branches and a workforce of 69,701. Its combined Assets Under Management (AUM) amounts to $950 Million. It provides a wide range of financial products and services to individuals, businesses, and institutions.

Problem Statement

The NBFC faced challenges in generating financial reports and loan agreements manually, which was time-consuming and prone to errors. They needed a solution that could automate the process and improve accuracy.

Our Solution

Document Automation: Automating document processing tasks like data extraction and classification. Improve accuracy and reduce manual errors.
AI-Powered Document Processing: Used AI to classify, extract, and validate data from documents. Automate document processing and reduce manual labor.
Intelligent Document Processing (IDP): Leverage AI to process and extract data from structured and unstructured documents. Automate document processing and expand capabilities.
Document Management with AI: Use AI for advanced document storage, retrieval, and user-friendly interfaces. Automate document management and improve efficiency.
Data Extraction and Validation: Employ AI to accurately extract and validate data from documents. Automate data processing and reduce manual errors.
Document Classification and Redaction: Utilize AI for document categorization and sensitive information redaction. Automate document organization and ensure data security.

Business Benefits

Time Savings: Up to 98% of data extraction accuracy from any document layout
Captures data up to 10 times faster than manual entry
Reduces processing time by half or more with document management software
Cost Savings: $878,000 annually saved by reducing staff miscalculations in finance departments
30.1% CAGR growth in the global intelligent document processing market from 2023 to 2030
Error Reduction: Reduces errors by using automated systems
Less than 1% error rate in document processing
Business Growth: Total ROI in less than 12 months and payback in less than 18 months for paperless documentation

Personalized Email Marketing for Financial Products

We developed a hyper-personalization platform for an investment firm, integrating portfolio analysis, segmentation, personalized recommendations, content generation, & predictive analytics.

An American multinational investment bank and financial services company with a presence in 15+ countries. Our client provides wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals.

Problem Statement

The firm’s existing email marketing campaigns relied on generic, one-size-fits-all messaging that failed to resonate with individual clients. Despite having detailed data on each client’s financial situation, risk tolerance, and investment goals, the marketing team struggled to effectively leverage this information to craft targeted and relevant communications. As a result, email open rates, click-through rates, and conversion rates were suboptimal, limiting the potential impact of their marketing efforts.

Our Solution

The Investment management firm partnered with us to deploy a hyper-personalization platform for their email marketing campaigns. The key components of the solution included:

Portfolio Analysis: The platform integrated with the firm’s portfolio management systems to gather comprehensive data on each client’s current investments, including asset allocation, performance, and risk metrics.
AI-Powered Segmentation: Advanced machine learning algorithms were used to analyze the client data and automatically segment the audience into highly targeted groups based on factors such as investment style, risk profile, and portfolio gaps.
Personalized Product Recommendations: The AI models identified specific investment products that could complement each client’s existing portfolio, taking into account their unique goals, time horizon, and risk tolerance. This allowed the firm to provide tailored recommendations for products like mutual funds, ETFs, and alternative investments.
Personalized Content Generation: Natural language processing (NLP) and Gen AI models were employed to create unique, hyper-personalized email content for each client segment. This included personalized product descriptions, performance projections, and investment rationales.
Predictive Analytics: The platform leveraged predictive analytics to forecast client behavior and identify the optimal time, channel, and frequency for delivering personalized email communications to each individual.

Business Benefits

Increased Open Rates: Email open rates increased by 32% as clients were more likely to engage with personalized content that addressed their specific investment needs and goals.
Higher Click-Through Rates: Click-through rates on personalized email campaigns were 40% higher compared to generic campaigns, indicating greater relevance and appeal to clients.
Improved Conversion Rates: Conversion rates for personalized email campaigns were 3 times higher than generic campaigns, leading to a significant increase in product sales and revenue.
Reduced Customer Churn: The solution reduced customer churn by 10% through personalized content that resonated with customers.

AI Financial Coach

Our AI-powered Financial Coach uses Generative AI for analysis, NLP for reporting, and offers personalized planning and education for clients.

Our Customer is a financial services corporation that, through its affiliates and subsidiaries, advises, and originates, trades, manages, and distributes capital for institutions, governments, and individuals. The company operates in three business segments: Institutional Securities, Wealth Management, and Investment Management.

Problem Statement

The company faced challenges in providing personalized financial advice to its clients, with financial advisors spending significant time on manual analysis and reporting. This process was time-consuming and often led to suboptimal financial planning and investment decisions.

Our Solution

We developed an AI-powered Financial Coach solution using a hybrid approach that combines Generative AI and Natural Language Processing (NLP). This solution consists of:

Generative AI for Financial Analysis: Analyzes financial data and market trends to identify potential investment opportunities and risks.
Natural Language Processing (NLP) for Financial Reporting: Analyzes financial reports and statements to identify key financial metrics and trends.
AI-powered Financial Planning: Provides personalized financial planning and investment advice based on clients’ financial goals and risk tolerance.
Client Engagement and Education: Provides clients with educational resources and tools to improve their financial literacy and understanding.

Business Benefits

Reduced Advisor Time: The solution reduced advisor time spent on manual analysis and reporting by 30%.
Increased Client Engagement: The solution increased client engagement and education by providing accessible and actionable financial information.
Improved Financial Literacy: The solution improved financial literacy among clients by providing educational resources and tools.
Cost Savings: The solution resulted in cost savings of $500,000 per year, primarily due to reduced advisor time and improved financial planning.

Documentation Compliance Automation

A Danish shipping and logistics company founded in 2005, with activities in shipping, ports, supply chain, and warehousing. It operates in 35 countries with over 15,000 employees worldwide.

A Danish shipping and logistics company founded in 1995. The company’s business activities include shipping, port operation, supply chain management, and warehousing. Our client operates in 35 countries and has over 15,000 employees worldwide.

Problem Statement

The company’s existing documentation compliance workflows were highly manual, time-consuming, and prone to errors. Employees were tasked with manually reviewing, verifying, and filing a vast array of documents, including shipping manifests, customs declarations, safety certifications, and supplier contracts. This resulted in significant delays, inconsistencies, and compliance issues, which often led to fines, shipment delays, and strained relationships with regulatory bodies and supply chain partners.

Our Solution

The supply chain management company implemented a comprehensive documentation compliance automation platform with the following key components:

Document Capture and Extraction: Advanced optical character recognition (OCR) and natural language processing (NLP) technologies were used to automatically capture, extract, and digitize relevant data from various document types, eliminating the need for manual data entry.
Intelligent Document Classification: Machine learning algorithms were trained to categorize and organize the documents based on type, content, and compliance requirements, ensuring that each document was routed to the appropriate workflow.
Automated Compliance Validation: The platform incorporated a comprehensive database of regulatory requirements, industry standards, and company policies, which it used to automatically validate the accuracy and completeness of the documentation, flagging any discrepancies or non-compliant elements.
Workflow Automation: The system streamlined the review, approval, and filing processes by automatically routing documents to the relevant stakeholders, tracking progress, and generating alerts for pending tasks or upcoming deadlines.
Centralized Document Repository: All documents and associated data were stored in a secure, cloud-based repository, providing a single source of truth and enabling easy retrieval, reporting, and auditing.
Continuous Improvement: The platform continuously monitored compliance trends, identified areas for improvement, and provided data-driven recommendations to optimize the documentation processes over time.

Business Benefits

Improved Compliance: The automation platform reduced the incidence of documentation errors and non-compliance by 42%, significantly mitigating the company’s regulatory and reputational risks.
Enhanced Operational Efficiency: By automating repetitive tasks and streamlining workflows, the company achieved a 35% reduction in the time and resources required for document processing and compliance management.
Increased Productivity: Employees were able to redirect their efforts from manual, administrative tasks to more strategic, value-added activities, leading to a 22% increase in overall productivity.
Better Supply Chain Collaboration: The centralized document repository and real-time visibility enabled the company to share information more effectively with its supply chain partners, improving coordination and reducing delays.

Enhancing Financial Reporting Accuracy with AI Mismatch Tracking

A renowned global leader in innovative bicycle design and high-quality manufacturing, offering a diverse range of bikes for casual riders, adventurers, and professional athletes.

A large multinational corporation recognized the need to improve the accuracy and efficiency of its financial reporting processes. Recurring reporting mismatches between various business units and the corporate finance team were leading to delays, errors, and increased operational costs.

Problem Statement

The company’s existing financial reporting workflow relied heavily on manual reviews and reconciliations, which were time-consuming, error-prone, and lacked scalability as the business grew. Discrepancies between the data reported by individual business units and the consolidated figures at the corporate level often went undetected until late in the reporting cycle, necessitating extensive investigations and rework. This resulted in delayed financial statements, reduced confidence in the reported numbers, and increased regulatory compliance risks.

Our Solution

The corporation partnered with us to implement an intelligent mismatch tracking system with the following key components:

Data Aggregation and Normalization: The system seamlessly integrated with the company’s enterprise resource planning (ERP), general ledger, and other financial systems to gather all relevant data points for reporting. Advanced data extraction and transformation techniques were used to normalize the data into a standardized format.
AI-Powered Mismatch Detection: Machine learning algorithms were trained on historical reporting data to identify patterns, trends, and anomalies that could indicate potential mismatches between business unit reports and the consolidated figures. The AI models continuously monitored the incoming data and flagged any discrepancies in real-time.
Root Cause Analysis: When a mismatch was detected, the system automatically performed a deep-dive analysis to identify the underlying causes, such as incorrect account mappings, data entry errors, or timing differences. This enabled the finance team to quickly address the issues at the source.
Automated Reconciliation: The platform provided intelligent reconciliation tools that could automatically match and validate the reported figures, reducing the manual effort required. It also generated detailed audit trails and supporting documentation to ensure transparency and compliance.
Predictive Insights: By analyzing historical mismatch patterns and their impact on the reporting process, the AI models were able to provide predictive insights and recommendations to the finance team. This allowed them to proactively address potential issues and optimize the overall reporting workflow.

Business Benefits

Improved Reporting Accuracy: The AI-powered mismatch tracking system reduced the incidence of reporting errors and mismatches by 42%, enhancing the reliability of the company’s financial statements.
Increased Efficiency: The automation of data aggregation, mismatch detection, and reconciliation tasks led to a 35% reduction in the time and effort required for the monthly and quarterly reporting cycles.
Enhanced Compliance: The detailed audit trails and root cause analysis capabilities of the system helped the company strengthen its internal controls and meet regulatory reporting requirements with greater ease.
Faster Insights: The predictive analytics and recommendations provided by the AI models enabled the finance team to make more informed decisions and identify opportunities for process improvements.

Customer Onboarding for Logistics

A global logistics leader providing efficient, reliable supply chain solutions for multinationals, optimizing goods flow through e-commerce fulfillment and warehouse automation.

Our Client is a world leader in providing cutting-edge logistics solutions for multinational companies and blue-chip market leaders. The sole focus of their business is on moving goods through supply chains with critical efficiency and reliability. They help customers get the most out of their supply chains by improving the flow of goods, from e-commerce fulfillment and flexible distribution to warehouse automation and reverse logistics.

Problem Statement

The logistics company’s existing onboarding process was fragmented, manual, and time-consuming for both customers and internal teams. New customers often faced lengthy delays, repetitive information requests, and a lack of transparency throughout the onboarding journey. This resulted in a poor first impression, increased operational costs, and higher rates of customer attrition in the early stages of the relationship.

Our Solution

We implemented a comprehensive customer onboarding platform with the following key components:

AI Avatar Creation: We built an AI powered customer onboarding program where an AI avatar took the customer through the platform and through the onboarding process step-by-step. The portal provided clear checklists, instructions, and progress tracking, reducing the need for manual hand-holding.
Intelligent Document Management: Advanced optical character recognition (OCR) and natural language processing (NLP) were used to automatically extract and populate key data points from customer documents, such as contracts, licenses, and invoices.
Collaborative Workflows: The platform enabled seamless collaboration between the customer and the logistics provider’s cross-functional teams, including sales, operations, and IT. Automated notifications, task assignments, and status updates ensured that all stakeholders remained aligned and accountable throughout the onboarding process.
Self-Service Knowledge Base: A comprehensive knowledge base was developed to provide new customers with on-demand access to FAQs, how-to guides, and troubleshooting resources. This empowered customers to find answers quickly and reduced the burden on the provider’s support teams.

Business Benefits

Reduced Churn: Early-stage customer churn rates decreased by 18%, as more customers successfully navigated the onboarding process and established long-term relationships with the logistics provider.
The repetitive data entry and reduced onboarding time was reduced by 30% with the help of OCR and NLP utilization.
Increased Operational Efficiency: The automation and digitization of onboarding tasks led to a 22% reduction in operational costs associated with onboarding new customers.
Enhanced Employee Productivity: By providing clear workflows and reducing manual tasks, the onboarding platform freed up the logistics provider’s teams to focus on higher-value activities and strategic initiatives.

Employee Assistant

A leading contract logistics provider, combining value-added services and management expertise to deliver customized, integrated solutions that drive efficiency, enhance quality, and create a competitive advantage for clients.

Our clients stands as one of the world’s leading contract logistics provider. Their core strengths lie in combining value-added services with management expertise, resulting in customized, integrated logistics solutions. These solutions drive efficiency, enhance quality, and create a competitive advantage for their clients.

Problem Statement

The supply chain professionals at the company were often overwhelmed by the volume and complexity of data, reports, and operational tasks they had to manage on a daily basis. This led to inefficiencies, inconsistencies, and suboptimal decision-making, which in turn impacted the overall supply chain performance. The leadership team sought to find a solution that could augment the human capabilities of their employees and drive greater operational excellence.

Our Solution

The supply chain management company partnered with us to develop a comprehensive employee assistant platform with the following key features:

Natural Language Processing (NLP): The assistant utilized advanced NLP models to understand the context and intent behind employees’ queries, allowing them to engage in natural, conversational interactions.
Knowledge Aggregation: The platform ingested and synthesized data from the company’s various enterprise systems, including ERP, WMS, TMS, and CRM, to provide employees with a unified view of supply chain information.
Predictive Analytics: Machine learning algorithms were trained on historical supply chain data to forecast demand, identify potential disruptions, and recommend optimal inventory, transportation, and logistics strategies.
Automated Workflows: The assistant was integrated with the company’s core business processes, such as purchase order management, shipment tracking, and supplier onboarding, to automate repetitive tasks and streamline operations.
Personalized Recommendations: The platform leveraged employee profiles, preferences, and past interactions to provide personalized insights, suggestions, and decision support tailored to each user’s role and needs.
Conversational Interface: Employees could access the assistant through a user-friendly, conversational interface, accessible via desktop, mobile, and messaging platforms, enabling seamless integration into their daily workflows.

Business Benefits

Increased Productivity: Supply chain professionals reported a 27% increase in productivity, as the assistant helped them quickly find information, automate tasks, and make more informed decisions.
Improved Decision-Making: The predictive analytics and personalized recommendations provided by the assistant led to a 19% reduction in supply chain disruptions and a 15% improvement in inventory optimization.
Enhanced Collaboration: The centralized knowledge base and automated workflows facilitated better cross-functional collaboration, breaking down silos and improving overall supply chain coordination.
Higher Employee Satisfaction: Surveys indicated a 22% increase in employee satisfaction, as the assistant alleviated the administrative burden and empowered supply chain professionals to focus on more strategic, value-added activities.

Supply Chain Risk Management

A global leader in food product sales, marketing, and distribution, with a network of specialty suppliers and 333 facilities worldwide. They serve approximately 7,000 customer locations across North America.

A global leader in selling, marketing, and distributing food products, as well as equipment and supplies for the food, service and hospitality industries. With a network of specialty suppliers and a distribution network of 333 facilities worldwide, they serve approximately 7,000 customer locations across North America

Problem Statement

The company faced significant challenges in managing supply chain risks, including single supplier sourcing, capacity constraints, and supply quality issues. This led to disruptions and potential losses.

Our Solution

We developed a Supply Chain Risk Management (SCRM) solution using a hybrid approach that combines Generative AI and Natural Language Processing (NLP). This solution consists of:

Generative AI for Risk Identification: Analyzes enterprise-wide supply chain data to identify potential risks and vulnerabilities and defining risk thresholds that trigger human validation and approvals
Natural Language Processing (NLP) for Risk Mitigation: Analyzes supplier contracts and agreements to identify potential risks and vulnerabilities.
AI-powered Supply Chain Risk Management: Provides real-time monitoring and alerting for potential supply chain disruptions.
Client Engagement and Education: Provides clients with educational resources and tools to improve their supply chain risk management capabilities.

Business Benefits

Reduced Supply Chain Risks: The solution reduced supply chain risks by 30% through effective risk identification and mitigation.
Improved Supply Chain Resilience: The solution improved supply chain resilience by 25% through predictive analytics and real-time monitoring.
Increased Supply Chain Efficiency: The solution increased supply chain efficiency by 20% through optimized risk management and mitigation strategies.
Cost Savings: The solution resulted in cost savings of $300,000 per year through reduced supply chain disruptions and improved supply chain resilience.

Automation of Bill of Lading

A logistics company helps organizations plan, implement, and execute product, material, and service movement and storage throughout the supply chain from origin to consumption.

A leading logistics company, helping organizations plan, implement, and execute the movement and storage of products, materials, and services throughout the supply chain from the point of origin to the point of consumption.

Problem Statement

The company faced challenges in managing their BL process, including manual data entry, errors, and delays. They needed a solution that could automate the process, reduce errors, and improve efficiency.

Our Solution

We used OCR and NLP to extract data from relevant documents, such as invoices and packaging lists. NLP recognizes and extracts relevant details from unstructured data, whereas OCR identifies characters and structures in scanned documents.
After being extracted, the data is organized and used to create BLs. Automation guarantees that the extracted data is precisely entered into the BL templates, producing BLs that are standardized and error-free.
Document process automation solutions simplify internal procedures by automating the review and approval procedures. This along with compliance and accuracy standards and speeds up the deployment of BLs.

Business Benefits

With the help of Automated data collection and document generation, processing times was greatly reduced, expediting the delivery of products and customs clearance.
Automated software results in significant Labour cost savings by reducing the number of employees required for data entry and customs document management.
Automation saves operating costs by doing away with the requirement for physical document maintenance and storage.
Elimination of human error and improved procedures reduced delays caused by mistaken data or missing documents.
Automation freed up staff to work on other important customs management projects, increasing productivity and resource utilization.
Pre-made templates and automated inspections ensured compliance with all customs requirements, minimizing the possibility of delays and penalties for noncompliance.

Code Conversion and Generation

We developed a code conversion solution analyzing source code, using ML to improve accuracy, and employing Gen AI for efficient code generation.

A company dedicated to alternative investment management. It operates in four key businesses areas: private equity with minority investments in companies, corporate partnerships, and some startups.

Problem Statement

The company faced challenges in converting and generating code efficiently for their finance product, leading to delays and errors in their development process. They needed a solution that could automate code conversion and generation, ensuring accuracy and speed.

Our Solution

We developed a code conversion and generation solution that consisted of:

Analyzing the source code to identify patterns and structures, allowing for accurate conversion and generation.
Using machine learning algorithms to learn from the analyzed code and improve the conversion and generation process.
Using Gen AI to generate code in the target language, ensuring accuracy and efficiency.
Also, The solution generated code in the target language, ensuring accuracy and efficiency.

Business Benefits

Improved Code Conversion: The solution improved code conversion by 90%, reducing errors and delays in the development process.
Increased Efficiency: The solution increased efficiency by 80%, allowing developers to focus on higher-level tasks.
Enhanced Accuracy: The solution enhanced accuracy by 95%, ensuring that the generated code was correct and reliable.
Reduced Costs: The solution reduced costs by 75%, as the need for manual code conversion and generation was eliminated.

Transforming Data Insights through Effective Knowledge Management

We have developed a knowledge management platform featuring a searchable repository, expert directory, collaborative tools, continuous learning, and curated content promotion.

A leading data analytics firm recognized the need to optimize its knowledge management practices to better leverage the expertise and insights generated across the organization.

Problem Statement

The data organization had amassed a wealth of valuable data, analysis, and domain knowledge, but this information was siloed across various teams and systems. Employees often struggled to access the right information at the right time, leading to duplicated efforts, inconsistent analysis, and missed opportunities to drive business impact. The leadership team understood that improving knowledge management would be crucial to maintaining the firm’s competitive edge.

Our Solution

The data analytics firm implemented a comprehensive knowledge management platform with the following key components:

Knowledge Repository: A centralized, searchable database was created to store and organize all of the firm’s data assets, analysis reports, methodologies, and other relevant knowledge resources. Advanced metadata tagging and natural language processing enabled efficient search and retrieval.
Expert Directory: An internal directory was developed to map the expertise and specializations of the firm’s employees. This allowed teams to quickly identify and connect with subject matter experts across the organization.
Collaborative Tools: Collaboration features, such as discussion forums, project workspaces, and real-time document co-authoring, were integrated to facilitate knowledge sharing and cross-pollination of ideas.
Continuous Learning: The platform incorporated machine learning algorithms to analyze user interactions and content engagement, providing personalized recommendations and surfacing relevant knowledge based on individual needs and behaviors.
Knowledge Curation: A dedicated team of knowledge managers was responsible for maintaining data quality, curating content, and promoting knowledge management best practices across the organization.

Business Benefits

Improved Productivity: Employees reported a 27% reduction in time spent searching for information, allowing them to focus more on high-value analytical work.
Enhanced Collaboration: The use of collaborative tools led to a 35% increase in cross-team knowledge sharing and ideation.
Faster Time-to-Insight: The centralized knowledge repository and personalized recommendations enabled analysts to generate insights 20% faster, accelerating the firm’s ability to deliver business impact.
Increased Employee Satisfaction: Survey results indicated a 22% improvement in employee satisfaction due to the enhanced access to information and subject matter expertise.

Gen AI in Product Recommendation

We implemented a Generative AI-powered personalization platform integrating CRM data, advanced AI models for product recommendations, contextual awareness, continuous learning, and omnichannel deployment.

Our Client has an extensive selection of paper and plastic envelopes for various needs. Whether you’re planning an event or sending holiday cards, their collection features a diverse range of colors, styles, and sizes.

Problem Statement

The retailer’s existing recommendation system relied on rule-based algorithms and collaborative filtering, which often failed to capture the nuances of individual customer preferences and behaviors. This resulted in generic, one-size-fits-all recommendations that did not resonate with customers, leading to suboptimal conversion rates and missed opportunities for cross-selling and upselling.

Our Solution

We implemented a Generative AI-powered personalization platform with the following key components:

Customer Data Integration: The platform seamlessly integrated with the retailer’s customer relationship management (CRM) system, e-commerce platform, and other data sources to gather a comprehensive view of each customer’s preferences, purchase history, and browsing behavior.
Generative AI Models: Advanced Generative AI models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), were trained on the customer data to learn the underlying patterns and relationships. These models were then used to generate personalized product recommendations for each individual customer.
Contextual Awareness: The Generative AI models incorporated contextual factors, such as the customer’s current browsing session, location, device, and time of day, to provide highly relevant and timely recommendations.
Continuous Learning: The platform continuously monitored customer interactions and feedback to refine the Generative AI models, ensuring that the recommendations became more accurate and personalized over time.
Omnichannel Deployment: The personalized product recommendations were delivered across multiple customer touchpoints, including the e-commerce website, mobile app, email campaigns, and social media channels, providing a seamless and consistent experience.

Business Benefits

Increased Conversion Rates: The Generative AI-powered personalization platform led to a 28% increase in conversion rates, as customers were more likely to engage with and purchase the recommended products.
Enhanced Cross-Selling and Upselling: By identifying relevant cross-sell and upsell opportunities based on customer behavior, the platform contributed to a 19% increase in average order value.
Improved Customer Satisfaction: Customer satisfaction scores increased by 32% as the personalized recommendations provided a more tailored and enjoyable shopping experience.
Increased Sales Revenue: The combined impact of higher conversion rates, average order value, and customer satisfaction resulted in a 25% year-over-year increase in sales revenue for the e-commerce retailer.

Dynamic Network Optimization

Our client is one of the largest communication technology companies globally. Formed in 2010, it is a leading provider of technology and communications services. Headquartered in San Francisco. The company offers voice, data, and video services on its award-winning networks, meeting customer’s demands for mobility, reliable network connectivity, and security

Problem Statement

The telecom company’s existing network management practices relied on static, manual configurations that struggled to keep pace with rapidly changing traffic patterns, user demands, and network conditions. This resulted in suboptimal resource utilization, service quality issues, and increased operational costs. The leadership recognized the need for a more agile, data-driven approach to network optimization.

Our Solution

The telecom provider partnered with a technology vendor to deploy an AI-powered dynamic network optimization platform with the following key components:

Real-Time Network Monitoring: The platform leveraged a comprehensive set of sensors and telemetry data to continuously monitor the network’s performance, including traffic flows, link utilization, equipment health, and customer experience metrics.
Predictive Analytics: Machine learning models were trained on historical network data to forecast future demand, identify potential bottlenecks, and predict the impact of network changes. This enabled proactive optimization rather than reactive troubleshooting.
Closed-Loop Optimization: The platform automatically adjusted network configurations, such as routing, capacity allocation, and resource provisioning, to maintain optimal performance based on the real-time and predicted conditions. This closed-loop process ensured that the network was continuously optimized.
Generative AI for Network Design: Generative AI models were employed to explore a vast design space and generate innovative network topologies, equipment placements, and configuration parameters. These AI-generated designs were then evaluated and validated against the company’s technical and business requirements.
Augmented Reality Visualization: An AR-based network visualization tool was provided to network engineers, allowing them to overlay real-time performance data and optimization recommendations onto a digital twin of the physical network infrastructure.

Business Benefits

Improved Network Performance: The dynamic network optimization platform led to a 22% reduction in network congestion, a 15% increase in throughput, and a 30% decrease in service outages.
Enhanced Resource Utilization: By dynamically allocating network resources based on demand, the telecom provider achieved a 28% improvement in overall equipment utilization.
Reduced Operational Costs: The automated optimization and proactive maintenance capabilities resulted in a 19% decrease in network operating expenses.
Faster Time-to-Market: The generative AI-powered network design process enabled the telecom company to launch new services and infrastructure upgrades 30% faster than before.

Test Case Generation

We integrated an AI-powered test case generation platform with static code analysis, ML models, NLP, GANs for synthetic data, and continuous testing.

A software and data company that revolutionizes advertising by creating a data-driven ecosystem. Our client’s platform unifies audiences across traditional TV, streaming video, and digital media. It allows advertisers to connect media exposures to sales.

Problem Statement

The company’s existing manual approach to test case design was time-consuming, error-prone, and often failed to uncover critical software defects. As the codebase and product complexity grew, the testing team struggled to keep up with the pace of development, leading to delayed product releases and increased technical debt.

Our Solution

We helped our clients develop and integrate an AI-powered test case generation platform with the following key components:

Static Code Analysis: The system performed in-depth analysis of the source code, identifying key functionalities, data flows, and potential failure points. This provided the foundation for generating targeted test cases.
Machine Learning Models: Advanced machine learning algorithms were trained on the company’s historical test cases and defect data to learn patterns and relationships within the software under test. This enabled the generation of more intelligent, coverage-optimized test cases.
Natural Language Processing (NLP): NLP techniques were used to extract relevant information from software requirements, user stories, and design documents. This allowed the test case generation to be aligned with the intended product behavior.
Generative Adversarial Networks (GANs): The platform employed GAN models to create synthetic test data that mimicked real-world user inputs and edge cases, expanding the breadth of test coverage.
Continuous Testing Integration: The generated test cases were seamlessly integrated into the company’s continuous integration and delivery (CI/CD) pipeline, enabling rapid feedback loops and automated regression testing.

Business Benefits

Improved Test Coverage: The AI-driven test case generation resulted in a 35% increase in code coverage, ensuring that a greater proportion of the software’s functionality was thoroughly tested.
Reduced Testing Time: By automating the test case design process, the company was able to reduce the time spent on manual test case creation by 42%.
Earlier Defect Detection: The AI-powered test cases were more effective at uncovering critical defects earlier in the development lifecycle, leading to a 27% reduction in post-release bugs.
Increased Agility: The seamless integration of the test case generation platform into the CI/CD pipeline enabled the testing team to keep pace with the rapid development cycles, supporting the company’s agile transformation.

Automating RFP using Gen AI

We automated the RFP process using Gen AI for content generation, NLP for understanding and customization, data analytics for insights, and scalability.

A company that focuses on building the infrastructure layer to form trusted business relationships quickly. They provide a network of identity and business management products, breaking down barriers and helping customers grow faster, safer, and smarter.

Problem Statement

Our Solution

We developed a comprehensive solution that leveraged Gen AI to automate the RFP process. The solution consisted of:

Automated Content Generation: Gen AI generated RFP content, including responses to specific requirements and questions, saving time and effort.
Natural Language Processing (NLP): NLP capabilities allowed Gen AI to understand and interpret natural language, enabling it to extract relevant information from RFP documents and generate contextually appropriate responses.
Customization and Personalization: Gen AI customized and personalized RFP responses based on the unique characteristics and preferences of the soliciting organization.
Data Analytics and Insights: Gen AI leveraged data analytics and machine learning algorithms to analyze past RFPs, historical data, and performance metrics, providing valuable insights for decision-making.
Scalability: Gen AI was highly scalable, allowing the company to handle a large volume of RFPs and responses efficiently.

Business Benefits

Improved Efficiency: The automated RFP process improved efficiency by 60%, reducing the time and effort required for manual content creation and data entry.
Enhanced Accuracy: Gen AI ensured accurate and consistent responses, reducing errors and improving the overall quality of the RFP process.
Increased Productivity: The automated RFP process increased productivity by 25%, allowing the company to focus on higher-value activities and strategic initiatives.
Cost Savings: The automated RFP process resulted in significant cost savings, reducing the need for manual labor and improving the overall cost-effectiveness of the process.
Improved Win Rates: Gen AI-driven insights helped with go/no-go decision-making and tailored proposals to better meet the criteria, significantly improving the chances of winning bids.
Faster Response Time: Project management and collaborative tools helped move teams along, and immediate access to up-to-date and approved information enabled project managers to fine-tune responses instead of searching for answers.

Voice Based Food Ordering in Restaurant

We developed a solution using NLP for voice-to-text conversion, automated order processing, error correction, and reporting for restaurant operations efficiency.

Our Customer has been leading restaurant chain for years in the United Kingdom. The chain offers a variety of breakfast, lunch, and dinner items but is well-known for their world-famous pancakes with 100+ outlets all around the country.

Problem Statement

The restaurant chain faced challenges in processing verbal orders at their drive-through windows and over the phone, leading to errors and frustration for customers. They needed a solution that could accurately transcribe verbal orders into text for cooks and servers.

Our Solution

We developed an AI-powered Enhanced Medical Search solution using a hybrid approach that combines Gen AI and Natural Language Processing (NLP). This solution consists of:

Voice-to-Text Conversion: We utilized advanced natural language processing (NLP) algorithms to convert spoken orders into text. The NLP engine was trained on a large corpus of restaurant-specific vocabulary and speech patterns to ensure accurate transcription.
Order Processing Automation: The transcribed text orders are automatically routed to the appropriate kitchen stations and server queues. The system integrates with the restaurant’s point-of-sale (POS) system to seamlessly process the orders.
Error Correction and Validation: The solution employs machine learning models to continuously improve the accuracy of the voice-to-text conversion. Real-time error detection and correction mechanisms are in place to identify and rectify any transcription mistakes.
User Interface and Reporting: The solution provides a user-friendly interface for kitchen staff and servers to view and manage the transcribed orders. Comprehensive reporting and analytics capabilities are included to track order accuracy, processing times, and customer satisfaction metrics.

Business Benefits

Reduced Time Spent on Data Entry: AI-powered document processing reduces time spent on data entry by up to 98%.
Increased Efficiency: The software streamlined the ordering process, reducing wait times and improving overall efficiency.
Reduced Errors: The software reduced errors in transcribing verbal orders, ensuring that customers received their orders accurately.
Enhanced Collaboration: AI transcription services enhance collaboration by providing instant, searchable notes and improving accessibility.

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