Service pillar
Custom AI Apps & Chatbots
When your users need answers they can ask for.
RAG-powered knowledge chatbots, branded conversational UIs, and custom LLM applications. Multi-tenant, production-grade, with citations users can actually trust.
What you get
- RAG architecture done right — hybrid search (BM25 + vector), reranking, citations, no hallucinated facts
- Multi-tenant chat infrastructure with auth, rate limits, and per-tenant knowledge isolation
- Embedded chat UIs that match your brand — Vercel AI SDK, assistant-ui, or fully custom
- Eval suite to catch drift before users do
- Cost telemetry — you'll know what every conversation cost and why
When this is the right fit
- Your users repeatedly ask the same questions your docs already answer
- You have a knowledge base no one reads
- Your support team is buried in tier-1 tickets
Sample builds
Customer-facing RAG knowledge bot
Citing answers from product docs, changelog, and policy pages. Embedded in your app, your help center, or your marketing site.
Internal ops assistant
Natural-language access to your CRM, ERP, or analytics warehouse — with audit trail and role-based access.
Vertical-specialist LLM app
A purpose-built app for a single workflow — contract review, claim processing, research synthesis — branded and shippable to your customers.
Tech we reach for
- Anthropic Claude
- OpenAI
- Vercel AI SDK
- pgvector / Pinecone / Qdrant
- Cohere reranking
- Clerk / Supabase Auth
- Langfuse
FAQ
Why not just use ChatGPT with file uploads?
It's a fine prototype. It's not a product. You can't multi-tenant it, brand it, embed it, version-control your prompts, run evals, or audit its outputs. We build the production version.
How do you stop the bot from making things up?
Citation-required prompting, RAG with reranking, refusal patterns when confidence is low, and a continuous eval suite. We measure hallucination rate weekly and treat any uptick as a P1.
Talk to a human about this.
20 min. No deck. We'll tell you what's possible — and what isn't.
