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AI Agents & RAG Chatbots

Production RAG agents grounded in your real data, with tool-calling and a measured hallucination rate.

Start this project →20-minute scoping call, then a fixed quote.

Who builds production RAG agents and AI chatbots?

Caffinix is an AI and backend engineering studio in Dhaka, Bangladesh that builds production RAG agents and chatbots grounded in a client's own documents, catalog or internal data. Every build ships with tool-calling into real systems, an eval set written from the client's actual questions before implementation starts, and a stated accuracy and hallucination rate rather than a demo. A build is fixed-scope: one production agent, its retrieval layer, the eval harness, deployment and handover.

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.

  1. 1
    Week 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
  2. 2
    Week 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
  3. 3
    Week 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
  4. 4
    Launch

    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.

Also booking now

Ready to start this?

Tell us what you're trying to ship. A senior engineer replies within one business day, and the first call is 20 minutes — we'll say straight whether it's a fit.

Or email us directly at contact@caffinix.com.

Prefer email? contact@caffinix.com

AI Agents & RAG Chatbots
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