General-purpose AI assistants put the burden on the user: know what to ask, know what context to paste, know how to phrase it. A tool built for one specific job removes all of that. It already knows which systems to read, which format to produce, and what "correct" looks like — because it was built for that job and nothing else.

Who this is for

Teams whose staff have quietly started pasting company data into public chat tools. Departments with a specific, repeated analytical or drafting task. Companies that want AI capability without their information leaving their control.

Problems we solve

Shadow AI use. Confidential material pasted into consumer tools because nothing sanctioned exists.
Prompt burden. Every user re-inventing the instructions, with inconsistent results.
No system access. A general assistant that cannot see the data the task actually needs.
Inconsistent output. The same task producing a different format each time.
Nothing to govern. No logging, no permissions, no visibility of what AI is being used for.

What we build

Task-specific assistants with the prompt, context and output format already built in
Direct, permission-aware access to your internal systems and documents
Interfaces that fit the workflow — a Slack command, a button in your admin, a scheduled digest
Structured, consistent output in the format the next step needs
Role-based access, so each team's assistant sees only its own data
Usage logging and cost attribution per team
Self-hosted deployment where information cannot leave your network

How we work

We watch the task being done before designing anything, because the useful tool is rarely the one people describe when asked. The interface goes where the work already happens rather than in a new application nobody opens. Assistants are built narrow and opinionated — one job done well beats a general tool that needs coaxing. Everything is logged so you can see what AI is actually being used for across the business.

Technologies we use

Claude, GPT and Gemini, or self-hosted Ollama where data residency requires it. Retrieval over internal documents with Qdrant or pgvector. Delivery through Slack, internal web tools, MCP servers or your existing admin interfaces. Laravel and Next.js for custom interfaces.

Business benefits

A sanctioned alternative to staff using consumer AI tools with company data
Consistent output because the prompt is engineered once, not per user
Tools usable without any AI expertise from the person using them
Governance: who used what, when, and at what cost
Institutional knowledge encoded into the tool rather than into individuals

Where it pays off

Proposal and quotation drafting from a product catalogue and past wins
Internal policy and HR assistants answering from the real handbook
Report generation from operational data on a schedule
Code and configuration review assistants for engineering teams
Research and competitor briefings assembled automatically
Meeting notes turned into structured actions in your task system

Common questions

Why not just buy licences for a commercial assistant?

For general-purpose use, often you should, and we will say so. Custom tools earn their place where the task is specific, repeated, and needs access to internal systems a commercial assistant cannot reach.

Can it run entirely on our infrastructure?

Yes, with self-hosted models. Quality is lower than frontier models and hardware costs are real, so we benchmark against your task before recommending it.

How do we control who sees what?

Permissions are enforced at retrieval, filtered by the user's identity before anything reaches the model — so restricted material is never in context to leak.

Staff pasting company data into public AI tools? A sanctioned internal tool is the practical fix.

Custom AI Tools & Internal Assistants