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01 · Service

AI & LLM Development Services

Applied AI that holds up in production — grounded, evaluated, and wired into real product flows.

The approach

Most AI projects don't fail at the model. They fail at everything around it: retrieval that returns the wrong chunks, outputs that don't parse, latency that makes a voice agent unusable, and no way to tell whether a change made things better or worse.

I build the parts that make an LLM feature safe to put in front of customers — retrieval before generation, structured output validation on every response, and grounding so an answer traces back to the source it came from. Then I wire it into the product properly: auth, rate limits, cost controls, and the UI that surrounds it.

This is work I've shipped, not surveyed. A RAG platform normalising multi-source data into explainable output. A voice agent platform holding sub-600ms on live calls with HIPAA compliance behind it. AI-assisted case analysis inside a Spring Boot dispute-resolution SaaS.

What you get

  • A retrieval pipeline — ingestion, chunking, embeddings, and pgvector search tuned against your actual corpus
  • Structured output contracts with validation, so downstream systems get reliable shapes instead of prose
  • Grounding and citation, so every answer traces back to the source data it came from
  • Voice agents on Retell, Twilio, ElevenLabs, or Deepgram with latency budgets that hold on live calls
  • Cost and rate-limit controls, prompt versioning, and a way to measure whether a change helped
  • The product engineering around it: API, auth, storage, dashboard, and deployment

Where this shipped

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