Impact in Action
Explore how we've helped industry leaders achieve tangible results with bespoke AI.
Automated Credit Scoring with an Agentic AI Framework
Challenge: A major finance lender had a manual, slow, and inconsistent loan application and credit scoring process, leading to operational delays.
Solution: Developed a full end-to-end agentic framework for the credit scoring and loan application system using modular, configurable AI agents.
Results:
- Drastically reduced loan processing time
- Improved consistency of credit risk assessments
AI Chatbot for Navigating Complex Labor Laws
Challenge: An Industrial Relations department was overwhelmed with complex queries about Indian Central and State labor laws, creating a knowledge bottleneck.
Solution: Spearheaded the creation of an AI-powered chatbot using LangChain and Azure AI Search to provide fast, accurate answers to compliance questions.
Results:
- Reduced time-to-answer for legal queries by over 90%
- Ensured consistent and accurate compliance guidance
Predicting Adverse Outcomes in Pregnancies
Challenge: A private hospital aimed to improve patient outcomes by proactively identifying at-risk pregnancies from historical clinical data.
Solution: Developed and deployed unique machine learning models to analyze clinical data and translate outputs into clinically actionable insights for medical staff.
Results:
- Enabled early identification of high-risk patients
- Provided clinicians with novel, data-driven insights
Synthetic Patient Data Generation
Challenge: A pharmaceutical company required large, privacy-compliant patient datasets for research but was limited by the availability of real-world data.
Solution: Directed the development of a GenAI tool that fine-tuned OpenAI GPT-4 and LLaMA models on custom datasets to generate high-quality synthetic patient data.
Results:
- Accelerated research timelines significantly
- Generated realistic data comparable to traditional models
GenAI-Powered Rules Engine for Call Centers
Challenge: Call center agents at a digital imaging company struggled with complex booking and billing policies, causing bottlenecks and long call times.
Solution: Led the development of an automation system using AWS Bedrock Claude to convert English text policies into an executable rule engine, assisting agents in real-time.
Results:
- Reduced process bottlenecks by over 50%
- Lowered average call handling time
Fire Ignition Prediction & MLOps
Challenge: A government department needed a reliable system to predict fire ignitions and manage the ML model lifecycle efficiently.
Solution: Trained a random forest model on a Databricks cluster using PySpark and established a full CI/CD MLOps workflow using Azure Pipelines for approvals and monitoring.
Results:
- Improved resource allocation for fire prevention
- Established a robust, automated MLOps pipeline