AI Solutions That Actually Work in Production

We design and build retrieval-augmented generation (RAG) systems that let your teams query internal documents, knowledge bases, and structured data with reliable, grounded answers. Our RAG pipelines use pgvector, Pinecone, or Weaviate for vector search — combined with LangChain or LlamaIndex for orchestration, chunking strategies, and reranking. We include evaluation pipelines from the start so you can measure answer quality, not just ship and hope.

We integrate GPT-4, Claude, Mistral, and AWS Bedrock models into your existing products and workflows via clean, maintainable Python APIs. Integration includes prompt engineering, structured output parsing, fallback logic, rate limiting, and cost tracking. We build evaluation harnesses that measure hallucination rate, latency, and task-specific performance — so you can upgrade models without breaking production.

We design AI-powered automations that process documents, extract structured data, classify content, and route work to the right systems or people. Built with Python, FastAPI, and n8n — connected to your CRM, ticketing system, or ERP via REST APIs. Common use cases: automated patient intake processing, invoice extraction, contract review triage, and support ticket classification.

We build AI-enabled analytics products that surface anomalies, predict outcomes, and recommend next-best actions — delivered as dashboards or embedded directly into your SaaS product. Built on Python data pipelines, PostgreSQL, and Next.js frontend — with model monitoring that alerts when prediction quality degrades in production.