Rottawhite — AI Systems Studio

AI copilots built on your own knowledge

Your domain expertise is trapped in documents, threads, and the heads of three people. This turns it into something the whole team can query.

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When this is the right call

You are probably here because

  • New hires take months to become useful because the answers they need are scattered across drives, inboxes, and tribal knowledge.
  • The same questions reach the same senior people every week, and answering them is not what you hired those people for.
  • You tried a generic search tool and it returned documents when what people actually needed was an answer.

What we build

AI Copilots & Assistants, concretely

Grounded question answering

Answers assembled from your own material, each one carrying citations to the source passage so a reader can verify rather than guess.

Document intelligence

Extraction and structuring of contracts, invoices, reports, and specifications, so unstructured files become data you can query and act on.

Copilots inside your tools

Assistants that live in Slack, Teams, or your own product, so the knowledge arrives where the work already happens instead of in another tab.

Permission-aware retrieval

Retrieval that respects who is allowed to see what, so a copilot never becomes an accidental route around your access controls.

Typical stack

Vector databasesClaude APIOpenAISlack / Teams SDKsPythonTypeScriptPostgreSQL

Good fit

This works best when

  • A body of documents that people need answers from, not just access to
  • Repeated internal questions that consume senior time
  • A need for citations, because an unverifiable answer is not usable

We take on 4-6 clients at a time and turn down work outside our expertise. If this is not the right fit we will say so on the call rather than after the invoice.

Common questions

How is this different from uploading files to ChatGPT?

A copilot runs continuously against your live sources, respects your permissions, cites what it used, and sits inside the tools your team already works in. Uploading files is a one-off that none of those things are true of.

How much of our data leaves our systems?

That is a design decision we make with you. We can keep retrieval and storage inside your own infrastructure and send only the assembled prompt to the model provider, or run the whole stack in your environment if your compliance position requires it.

What happens when documents change?

Sources are re-indexed on a schedule or on change, so answers reflect the current version. Stale retrieval is the most common reason these systems lose trust, so it is handled explicitly rather than left to chance.