AI Agents & RAG
Production AI agents — grounded retrieval (RAG), tool-using multi-agent systems, and chat assistants built on OpenAI and modern frameworks. Evaluated, guarded, and monitored so you can trust what it puts in front of users, not just a flashy demo.
Problems we solve
- Your AI demo works in the room but not in production.
- The model hallucinates or ignores your data.
- You need an assistant grounded in your own knowledge base.
- You don't know how to evaluate or safeguard AI outputs.
What you get
- Grounded, cited answers from your knowledge base
- Agent orchestration with real tool use
- Prompt evaluation suite & safety guardrails
- Vector search (Pinecone / Supabase) & monitoring
What success looks like
AI that's accurate, grounded, and safe to ship
Automations that take real action, not just chat
Confidence in quality via evals and monitoring
Common questions
What is RAG and do I need it?
Retrieval-augmented generation grounds the model in your own documents and data so answers are accurate and citeable instead of made up. If you want an assistant that knows your business, you need it.
Can the agent actually do things, not just answer?
Yes. I build tool-using and multi-agent systems that take real actions — updating records, sending messages, running workflows via n8n — with guardrails around them.
How do you keep AI outputs safe and reliable?
Evaluation suites, guardrails, and monitoring. We measure quality before launch and watch it in production so regressions get caught early.
Ready to start your ai agents & rag project?
Tell me what you're building and I'll respond within a few hours.