Service
Data & AI Engineering
The part nobody wants to fund, and the reason the rest fails.
The problem
Roughly one mid-market company in six has data it can actually build on. Data quality is the most cited barrier to AI adoption, ahead of integration and ahead of cost. It is also the least visible, which is why it gets skipped and why the project stalls three months in.
The layer everything else depends on
Scattered sources converge into a governed data layer with explicit, enforceable access rights. That layer is what makes structured data usable by both AI systems and ordinary operations. Without it, every project downstream stalls.
What we actually do
- Establish what data exists, where it lives, who owns it and whether anyone trusts it.
- Build the pipelines that keep it current, with tests that fail loudly rather than quietly.
- Make access rights explicit and enforceable, because this is what decides whether you can deploy at all.
- Instrument quality so degradation is caught by a monitor rather than by a user.
When this is the wrong answer. You want a full data platform before any use case exists. That is a multi-year programme with no feedback loop. Pick one revenue-adjacent process, make its data trustworthy, ship it, then compound.
Problems this solves