Services / Build

AI tools built on your data, shipped to production, handed over with the code.

For teams that already know the problem and need a working product: an internal knowledge assistant, workflow agents, connectors between AI and your tools, or a vision model on your images.

Custom AI Tools

Your team answers the same questions, re-reads the same documents and rewrites the same reports every week. Off-the-shelf chatbots cannot see your data, and the ones that can are hard to trust.

Timeline
4-8 weeks to a first production version
How we work
Fixed-scope build, optional support retainer
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What you get

  • Knowledge assistant over your documents that cites its sources
  • Workflow agents for inbox triage, meeting notes or recurring reports
  • Evaluation set and monitoring so quality is tracked after launch
  • Handover documentation; you own the code and the data

Proof

AI Workspace Integrations

Your AI assistant cannot see the tools where the work happens, so people copy and paste, and company data ends up in chats nobody controls.

Timeline
2-5 weeks
How we work
Fixed-scope build, optional support retainer
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What you get

  • MCP connectors for the tools your team uses
  • Data isolation per team or workspace, enforced in the connector
  • Sign-in with OAuth and passkeys instead of shared API keys
  • Read-only by default; write actions stay in dry-run until confirmed
  • Audit log of every tool call

Proof

Computer Vision

Counting, inspecting or tracking items by eye is slow and error-prone: stock on a shelf, produce on a line, parts on a tray.

Timeline
About 2 weeks for feasibility, 4-8 weeks to production
How we work
Feasibility sprint first, then a fixed-scope build
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What you get

  • Feasibility test on a sample of your images
  • Detection or counting model trained on your data
  • Web or mobile app that runs in real time
  • Accuracy report on images the model has never seen

Proof

Questions

Who owns the code?

You do. Everything is delivered in your repository, with documentation and an evaluation set so your team can change it safely.

Where does our data go?

It stays in accounts you control. Connectors are read-only by default and every write action needs explicit confirmation.

What if the first version is not good enough?

Quality is measured from day one with an evaluation set built from your real tasks, so "good enough" is a number agreed before the build starts.

Other services

Advise

Decide what to build with AI, and how you will know it worked.

Assure

Know how often your AI is wrong, where it can leak, and whether people can use it.