A major energy distributor struggled with manual FEMA reimbursement processing after a hurricane, causing bottlenecks and delays.

Accelerate and automate FEMA document processing to unlock recovery funds.

Led a team to build custom OCR models for FEMA forms. Used Azure Document Intelligence, PostgreSQL, and QC dashboards for review.

NY healthcare payors faced CMS penalties due to bad directory data.

Build audit simulation tools to improve data quality before federal audits.

Designed a rules-based and ML-augmented audit simulator. Created synthetic data, risk dashboards, and compliance scores.

A home healthcare provider was near bankruptcy with fragmented data and outdated systems.

Lead an enterprise data transformation for care, compliance, and financial ops.

Managed a 15-person team. Migrated data to Azure, implemented governance with Collibra, and built a Patient 360 solution.

A pharmaceutical tech firm needed a way to assist technicians with clinical questions.

Deliver a secure, LLM-powered chatbot for pharmacy techs.

Architected a solution using LangChain, CrewAI, and FAISS for RAG. Built with GPT-4 and added access controls.

MRI client lacked visibility into PI market prospects across fragmented data.

Build a unified CRM dataset from external legal and medical sources.

Created ML pipelines using record linkage and NLP. Unified bar, payer, and court data; integrated into Salesforce.

Medtech partner needed real-time tissue detection during colonoscopies.

Build a deep learning model for live abnormality detection.

Trained a PyTorch CNN on endoscopic video. Optimized for real-time inference and low latency.

A global CPG client's GenAI system was built on legacy AWS infrastructure.

Migrate to Azure and refactor for scalable GenAI workloads.

Re-architected platform using Azure Functions, SQL, and AKS. Containerized GPT microservices.

Automotive OEM faced battery defects with unclear causes.

Identify battery defects in real-time from factory cameras.

Built a hierarchical CNN with Keras/TensorFlow. Deployed explainable ML with Grad-CAM and MLOps integration.

Data mismatches across dev/staging/prod environments were delaying releases.

Ensure real-time data sync across environments.

Built CDC pipeline using Debezium, Kafka, and Postgres. Added schema validation and topic automation.

Engine manufacturer needed to predict engine failure from service logs.

Build a predictive NLP model for engine end-of-life.

Fine-tuned BERT for entity extraction. Built predictive pipeline and deployed with MLflow APIs.

Ford lacked data on customer driving behavior for hands-free features.

Reconstruct driving routes from sparse telematics data.

Used PySpark to process 10M+ events. Rebuilt routes using spatio-temporal clustering and test drive validation.

Owner's manual chatbot couldn't handle structured data.

Enable natural language querying of structured databases.

Enhanced transformer-based Text-to-SQL model with semantic parsing and schema grounding.

Built a Q&A chatbot on vehicle manuals for engineers and compliance teams.

Create a scalable, in-vehicle question-answering tool.

Combined ElasticSearch and BERT-based QA. Deployed to Ford's Kubernetes platform with SSO.