AI Agents & RAG Chatbots
Production RAG agents grounded in your real data, with tool-calling and a measured hallucination rate.
We build AI agents and RAG chatbots on a real backend: retrieval over your data, tool-calling into your systems, evals before launch, and a stated accuracy or hallucination rate. The goal is not a polished demo. It is an agent your team can safely put in front of customers or internal users.
What you get
- ✓Grounded answers from your documents, product catalog, or internal data
- ✓Tool-calling for real actions such as lookup, booking, order capture, or CRM updates
- ✓Eval report with measured accuracy, hallucination rate, and top failure modes
Who it's for
- —SaaS teams adding a customer-facing or internal AI assistant
- —E-commerce and marketplace teams that need product Q&A grounded in catalog data
- —Founders who tried a wrapper and now need a production agent that can be measured
How we work
A real engagement, week by week.
- 1Week 1
Data + task mapping
- Map the jobs the agent must handle and the answers it must refuse
- Ingest sample docs, catalog data, or knowledge-base content into a retrieval plan
- Define the eval set and launch threshold before implementation starts
- 2Week 2
Prototype with retrieval
- Build the first RAG flow with citations or source grounding where useful
- Add tool specs for actions such as lookup, order capture, or handoff
- Run early evals to expose retrieval gaps and prompt failure modes
- 3Week 3–4
Production hardening
- Tune chunking, retrieval, prompts, guardrails, and refusal behavior
- Wire auth, logging, rate limits, and deploy paths into your stack
- Re-measure accuracy and hallucination rate against the agreed eval set
- 4Launch
Handover + monitoring
- Ship runbooks, environment notes, and failure-mode documentation
- Train your team on how to update data and inspect agent behavior
- Optionally continue with regression monitoring and reliability support
Why us
Why us.
Nous and Iris prove the pattern: a Spring Boot + pgvector + Gemini agent grounded in real product data, not generic chatbot memory.
Every build ships with evals and a stated failure profile, so quality is measured instead of argued.
Senior backend depth means the agent can live inside production systems with auth, observability, and handoff paths.
Common questions
Things prospects ask first.
Yes. We start from the data you already have, then tell you where formatting, ownership, or freshness will hurt answer quality.
No. Chat is one interface. We can also expose the agent through your app, admin tools, APIs, Messenger, WhatsApp, or internal workflows.
We combine retrieval design, prompt boundaries, refusal rules, tool constraints, and evals. The deliverable includes measured failure modes, not a vague quality claim.
Yes. That is the point. We handle auth-aware tool calls, API contracts, logging, deployment, and handover instead of leaving you with a hosted toy.
Ready to start this?
20-minute scoping call. We'll tell you straight whether it's a fit.