AI that answers from your documents — and stays quiet when it doesn't know
Plugging a chatbot into your product takes an afternoon. Getting it to answer from your real prices, policies and records — without inventing what isn't there, and without locking you to one vendor's pricing — is the actual work. This is that build, running in production on a multi-tenant platform.
A chatbot wired to ChatGPT is not an AI integration.
That's a model that sounds great and knows nothing about your business — and when it doesn't know, it makes something up. A real integration starts with your data: what it can read, what it's allowed to answer, and what it does when the answer isn't there. You don't see the difference in the demo. You see it the day a customer gets a price you never offered.
Put in your numbers.
Gather your sources
PDFs, catalog, prices, policies, records — what already exists, without rewriting it.
Index and isolate
Chunked, indexed, and scoped per client so nothing mixes.
Answer above a confidence floor
If the answer isn't in your data with enough similarity, it doesn't invent one.
Act, then hand off
Look up, book or update in your systems — and pass to a person when it should.
Grounded in your own data
PDFs, URLs and records get chunked and indexed, and answers come from them — with a similarity floor, so below a confidence line the assistant stays quiet instead of guessing.
Agents that do things, not just chat
Tool calling against your real systems — look up a record, book a slot, update a file — with the model's arguments validated, never trusted blindly.
Any model, no lock-in
DeepSeek, OpenAI, Groq or OpenRouter behind one interface. Switching providers is two environment variables and a restart, not a refactor.
Handoff that actually works
A live inbox for your team, and the bot goes silent the moment a person takes over.
Costs that track usage
Cheap logic handles the common case, the expensive model fires only when it has to, and budget caps stop a runaway loop before it becomes an invoice.
Tick what happens to you today.
Melow: a multi-tenant AI platform in production — answers grounded in each client's own documents, four swappable LLM providers, and a clinical copilot that turns speech into structured patient records.
Before you ask
Will it make things up?
It's built not to. Answers come from your indexed documents, and below a 0.40 similarity floor the assistant says it doesn't know or hands off to a person instead of improvising.
Which AI model does it use?
Whichever fits your cost and privacy needs — DeepSeek, OpenAI, Groq or OpenRouter today. They sit behind one interface, so switching later is a config change, not a rewrite.
How long until it's live?
A focused integration — an assistant grounded in your documents — typically takes two to six weeks. The date goes in the quote next to the price.