I help startups turn fragile AI prototypes into reliable, production-ready agent and RAG systems that retrieve business context, call tools, integrate with existing APIs and escalate uncertain decisions to a human. Previously, I improved a production RAG pipeline from approximately 55% to 95%+ retrieval precision across 500+ daily queries, while reducing manual review through evaluation and human-approval workflows.

One focused AI agent or RAG workflow built around a real business use case

LLM integration using OpenAI, Claude or Gemini

Retrieval pipeline with PostgreSQL/pgvector, Qdrant or another suitable vector database

Tool calling and integration with your APIs or business software

Human-in-the-loop approval for sensitive actions

Basic evaluations, confidence checks and execution tracing

FastAPI backend and a lightweight Next.js interface when required

Deployment, documentation and complete source-code handoff

Internal knowledge copilots

Document and customer-support assistants

Research and data-processing agents

Inbox and ticket automation

Multi-step workflow agents

AI features for an existing SaaS product

We begin with a short discovery call to define one valuable workflow and measurable success criteria. I then design the architecture, build the system, test failure cases and deploy a working version.

The starting package covers one clearly scoped workflow with one primary data source or external integration. Larger multi-agent platforms, complex migrations and additional integrations will be scoped separately before development begins.