Ship a chatbot that answers from your data, not the internet
We build chatbots that answer from your own docs and data. Grounded answers with sources. No made-up facts. No thin wrapper around ChatGPT.
Last updated September 2026. US LLC in Wyoming. Our team works in your timezone.
The problem
Sound familiar?
Your support team answers the same questions every day
The answers are all in your docs. Nobody reads the docs. Your team answers the same ten questions on repeat.
GPT wrappers give wrong answers and lose user trust
You shipped a chatbot with no retrieval layer. It makes things up. Users catch it and stop using it. Tickets do not drop.
Your docs are scattered across Notion, Confluence, and PDFs
Your docs live in four places, written by three people, and go stale. Search returns the wrong page. Nobody can find anything.
You cannot tell if the AI is accurate
The chatbot is live. You have no quality scores or rejection rate. You learn about wrong answers when a customer complains.
What do we actually do?
We build the full RAG pipeline with evaluation built in from day one. The bot answers from your data, cites sources, and hands off when it does not know.
What changed in 2026?
In 2026 every product has a chat box, and users have learned to test it with a hard question in the first minute. A thin wrapper around a model with no retrieval fails that test. This build answers only from your documents, shows the source, and hands off when it does not know, with an accuracy score you can watch.
What's included?
- Document ingestion for PDF, Notion, Confluence, Markdown, or databases
- Chunking tuned to your content and queries
- Vector store on Pinecone, pgvector, or Weaviate
- Hybrid search, semantic plus keyword, for accuracy
- LLM grounding prompt with source citations
- Confidence threshold that routes unsure answers to a human
- Evaluation dashboards for accuracy, rejection rate, and latency
How it works
01.
Ingest
We map your sources and build the ingestion pipeline.
02.
Retrieve
We set up the vector store and tune search on your real queries.
03.
Generate
We wire the LLM with grounding, citations, and handoff.
04.
Evaluate
We benchmark accuracy and hand off with a live dashboard.
Proof it works
What we have already shipped
Case study
Pack Assist
8-week delivery, RAG + hybrid AI
Read the case study
Free tool
RAG Architecture Checklist
Free resource for Software Founders & Builders.
Open the free tool
The offer
Free 30-minute discovery call
We listen first. No pitch decks.
Book a discovery call
Keep exploring
More for Software Founders & Builders
Agentic Workflows & Automation
Replace manual multi-step work with AI agents that take action
View service
MVP Sprint
From zero to a deployed, revenue-ready product in 8 weeks
View service
Prototype Takeover
Your Lovable prototype broke when a second user signed up
View service
Fractional Engineering Team
A full engineering team on 30-day terms. No equity, no recruiting.
View service
Voice Agent Integration
Ship a real-time voice agent your users want to call
View service
ML-Driven Personalization
Personalization built into your product, not a third-party tag
View service