Are you building an AI product but struggling with
hallucinations, unreliable outputs, and systems that
break when your LLM API goes down? I build LLM agents
and RAG systems that work in production — not just demos.
WHAT YOU GET:
- LLM-powered agents with multi-step reasoning and
tool use (LangChain / LangGraph)
- RAG systems with vector search (PostgreSQL + pgvector)
for accurate, grounded responses
- Multi-provider LLM abstraction — swap between Groq,
OpenAI, Ollama with zero downtime
- Agentic workflows that plan, execute, and self-correct
- Graceful degradation — rule-based fallbacks at every
AI layer so your product never dies silently
- Fernet-encrypted API key management for secure
multi-tenant deployments
- Full test suite — 900+ tests across my portfolio
MY CORE PRINCIPLE:
AI explains, math computes. Every numerical output is
deterministic and verifiable. No hallucinated statistics.
This is not a feature — it is an architectural guarantee.
PROOF OF WORK:
- Aura Data Platform (ACE): ~16,500 lines of code, 914
automated tests, 49 API endpoints, self-learning RAG
via pgvector. Live at aura-aitech.com.
- Agent Scientist Studio: self-improving ML platform
with ACE architecture. Backed by a published
technical white paper.
MY STACK:
Python · FastAPI · LangChain · LangGraph · PostgreSQL ·
pgvector · Groq · OpenAI · Ollama · Docker · Linux
WHO THIS IS FOR:
- Startups building AI-first products
- Teams adding LLM capabilities to existing software
- Founders who need a reliable AI backend, not a demo
I start every engagement with a technical design doc
before writing a single line of code. You get clarity
upfront — no surprises mid-project. And I am happy to
start with a small paid milestone so you can verify
quality before any larger commitment.