Production systems we've built — RAG pipelines, autonomous agents, ML models, and intelligent automation deployed at real scale.
Multi-source RAG system indexing contracts and case files. Answers queries with source citations and access-level controls. See the full case study →
Autonomous agent reads QC SOPs, instructs operators step-by-step, logs results, detects deviations, and escalates anomalies in real time. See the full case study →
OCR + LayoutLM pipeline extracting and validating line items, vendor data, and totals from PDF invoices at high volume. See the full case study →
Domain-fine-tuned LLM with tool use for querying tickets, orders, and knowledge bases. Resolves a large share of tier-1 queries without human escalation.
Ensemble ML model combining XGBoost, Prophet, and a lightweight LSTM for multi-SKU demand forecasting across 40+ store locations.
Agentic system that reads contracts, identifies risk clauses, compares against playbooks, and generates structured review reports for lawyers.
HIPAA-compliant NLP pipeline extracting diagnoses, medications, and lab values from unstructured clinical notes into structured EHR schemas.
Graph ML + gradient boosting ensemble for transaction fraud detection, built for high-throughput, low-latency inference on AWS.
Adaptive AI tutor that tracks learner gaps, retrieves relevant curriculum, generates custom explanations, and simulates exams dynamically.
Project descriptions above are illustrative of representative engagements; specifics have been generalized to protect client confidentiality. See the detailed case studies below for full walkthroughs.
Illustrative walkthroughs of how we approach these engagements — challenge, approach, and result.
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