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RAG Chatbot and LLM Integration Development That Holds Up in Production

A demo chatbot is an afternoon. A support agent that resolves about 90% of real tickets, pulls live account data, knows who it is talking to and never invents a refund policy is an engineering project. I have shipped the second kind.

The approach is the same every time: start from the workflow, retrieve the right context, give the model typed tools rather than free-form actions, add guardrails and an eval set before launch, and track cost per resolved task after it.

Discuss your projectSee related work

What you get

  • Answers grounded in your documents and live systems, with citations where useful.

  • Role-aware behaviour: customers, agents and admins get different tools and tone.

  • Guardrails for off-topic, sensitive and high-risk requests.

  • Evals and dashboards so quality is measured, not assumed.

Deliverables

  1. 01

    Data audit and chunking / embedding strategy

  2. 02

    Retrieval pipeline with vector search and re-ranking

  3. 03

    Tool-calling agent on the Claude or OpenAI API

  4. 04

    Conversation memory and rolling summaries

  5. 05

    Eval set, guardrails and prompt-injection defenses

  6. 06

    Cost controls: prompt caching, model routing, budgets

Stack

  • Claude API
  • OpenAI API
  • Embeddings
  • Chroma / pgvector
  • LangChain
  • Node.js
  • Laravel
  • Next.js

Where I've done this

FAQ

What will an AI agent cost us per ticket?

Run your numbers through the cost-per-resolved-ticket calculator on this site (tools/ai-cost-calculator). It models prompt caching, resolution rate and model routing, which are the three levers that actually move the bill.

Which model should we use?

It depends on the task mix, latency and budget. I usually start with a strong model at low effort for quality, then measure whether a cheaper model holds up on your eval set before switching routes.

How do you stop the bot from making things up?

Retrieval with strict grounding instructions, typed tools for anything factual, refusal paths for unknowns, and an eval set that includes adversarial questions. The REConnect agent only answers from indexed data and live lookups.

Can it work inside our existing app?

Yes. I've integrated agents into Laravel and Node backends with role-aware access to the same data your app already has.