Data integration
Stop copying data between systems that should already talk.
Connect what you already haveThe problem.
The same customer lives in the CRM, the ERP and three spreadsheets, with three different addresses. People copy data from one screen to another all day, and every copy is a chance to get it wrong. It is also the layer every later AI project depends on, which is why skipping it is how those projects stall three months in.
How it works.
Test fails: loud, not silent
What we do.
- Establish what data exists, where it lives, who owns it and whether anyone trusts it.
- Connect the systems that should share it, with pipelines that keep it current.
- Test every pipeline so it fails loudly rather than quietly.
- Make access rights explicit and enforceable, because they decide whether anything built on top can leave the pilot group.
You want a full data platform before any use case exists. That is a multi-year programme with no feedback loop. Pick one process that matters, make its data trustworthy, ship it, then extend.
Not sure this is the one you need?
That is what the first call is for. Thirty minutes, no slides, and an honest answer even if it is not us.
Repetitive work handled by rules that give the same result every time. No model needed.
Custom applicationsA small internal tool built around how your team works, when no product fits.
AI, where it adds valueKnowledge assistants and agents, built only where language or judgement is the real work.