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Ship a chatbot that answers from your data, not the internet

We build retrieval-augmented generation systems that pull from your documents, support history, or product data and return accurate, grounded answers. No hallucinations. No wrappers around ChatGPT.

Hire Us on Upwork

Sound familiar?

Your support team answers the same questions every day
Users ask how the product works, what the pricing is, and where to find features. Every answer exists in your docs. Nobody reads the docs. Your team fields the same ten questions on repeat.
GPT wrappers give wrong answers and users stop trusting the product
You shipped a chatbot that calls the OpenAI API with no retrieval layer. It confabulates. Users catch it. They stop using it. The support ticket volume does not go down.
Your knowledge base is fragmented across Notion, Confluence, and PDFs
Documentation is in four places, written by three people, updated sporadically. Users cannot find anything. Your team cannot find anything. Search returns the wrong page every time.
You have no way to evaluate whether the AI is actually accurate
The chatbot is live. You have no metrics on answer quality, no rejection rate, no confidence scores. You find out it gave a wrong answer when a customer complains.

What we actually do

We build the full RAG pipeline — ingestion, chunking, vector storage, retrieval, and generation — with evaluation built in from day one. Your chatbot answers from your data, cites its sources, and escalates when it does not know.

What's included

Document ingestion pipeline — PDF, Notion, Confluence, Markdown, or database
Chunking strategy optimised for your content type and query patterns
Vector store setup — Pinecone, pgvector, or Weaviate based on your stack
Retrieval layer with hybrid search (semantic + keyword) for accuracy
LLM integration with grounding prompt and source citation
Confidence threshold and escalation logic — routes low-confidence queries to human
Evaluation framework — answer accuracy, rejection rate, and latency dashboards

How it works

Ingest

We map your knowledge sources and build the ingestion and chunking pipeline.

Retrieve

We build the vector store and tune the retrieval layer against your real queries.

Generate

We wire the LLM with grounding, citations, and escalation logic.

Evaluate

We run accuracy benchmarks and hand off with a live monitoring dashboard.

Pack Assist
8-week delivery, RAG + hybrid AI
Read the case study
From $10,000

Scoped per document volume and integration complexity. Most projects deliver in 4–8 weeks.

Frequently asked

Ready to get started?

Let's build your rag chatbot & knowledge system system

Hire Us on Upwork