Software Founders & Builders
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.
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.
The solution
What 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 you get
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
The process
How it works
Ingest
We map your sources and build the ingestion pipeline.
Retrieve
We set up the vector store and tune search on your real queries.
Generate
We wire the LLM with grounding, citations, and handoff.
Evaluate
We benchmark accuracy and hand off with a live dashboard.
Proof it works
The offer
Priced by document volume and integrations. Most projects ship in 4 to 8 weeks.
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Common questions
Frequently asked
01Which LLMs do you support?
OpenAI GPT-4o, Anthropic Claude, and Mistral. We pick based on your speed, cost, and accuracy needs.
02What if my documents change frequently?
New or updated docs re-index on their own. No manual re-runs.
03How do you prevent hallucinations?
The model only answers from retrieved context. If nothing relevant is found, it hands off instead of guessing.
04Can it handle multiple languages?
Yes. We use multilingual embeddings and LLMs. We scope this on the discovery call.
05Do we need a vector database subscription?
Pinecone and Weaviate have hosted plans. pgvector runs inside your existing Postgres at no extra cost. We recommend based on scale.
06Is TechEmulsion based offshore?
No. We operate through our US entity in Wyoming. Our team works in your timezone.