Chatbots that actually know your product.
A RAG chatbot answers customer questions using your own documentation — not generic AI guesses. We build the ingestion pipeline, the retrieval, the prompt scaffolding, the feedback loop, and the dashboards to keep an eye on it. End-to-end.
What RAG actually means.
A normal chatbot either follows a decision tree or asks an LLM, which then makes things up about your product because it has never read your documentation.
A RAG chatbot first retrieves the relevant chunks of your own data — manuals, FAQ pages, ticket history — and then generates an answer grounded in those retrieved chunks. The model still does the writing. Your data does the knowing.
The result: a chatbot that can answer questions you never explicitly programmed, with your terminology, your tone, and your actual facts.
Six pieces of production RAG.
The demo is the easy part. Everything below is what makes a chatbot worth keeping after the launch buzz.
Pinecone or pgvector
We pick the vector database based on your data size and query patterns. Pinecone for managed simplicity, pgvector when you want everything in one Postgres.
Claude, GPT, or open weights
Model selection driven by cost, latency, and quality — not vendor preference. We benchmark on your actual data before committing.
Self-improving loop
Every conversation is logged. Bad answers get flagged, root-caused, and fed back into the retrieval index. The bot gets better every week.
Read every conversation
Dashboards for response quality, latency, cost per query, drop-off points. You see what the bot is doing, all the time.
Spend caps that work
Per-user rate limits, prompt caching, model fallback when traffic spikes. We make sure a viral moment doesn't bankrupt you.
Yours to maintain
Architecture doc, eval suite, deployment scripts. We hand off everything — and stick around for the first three months of production.
Things people ask first.
What is a RAG chatbot?
RAG stands for Retrieval Augmented Generation. Instead of relying only on what a language model was trained on, a RAG chatbot retrieves relevant information from your own data — product docs, FAQs, past tickets — before generating an answer. This means the chatbot answers with your specific knowledge, not generic AI guesses.
How is a RAG chatbot different from a standard chatbot?
Standard chatbots follow decision trees or match keywords. RAG chatbots use a large language model (like Claude) that understands natural language, and ground its answers in your actual data via a vector database like Pinecone. The result is a chatbot that can answer questions you never explicitly programmed — because it reads and synthesises your documentation dynamically.
What data does a RAG chatbot need?
Any structured or semi-structured text: product documentation, FAQ pages, support ticket history, internal wikis, PDF manuals, website content. We handle the ingestion, chunking, embedding, and indexing. You don't need to re-format your existing content.
Build a chatbot that improves itself.
Tell us what your customers ask most often. We'll respond within one business day with a written proposal and a two-week proof of concept plan.
Start a project →