Discovery
A short, structured look at the systems, people and constraints already in play.
Service
Governance & AI Act
Catalogue, lineage, data quality and ownership, plus risk tiering and model cards that satisfy an auditor.
Governance that engineers can implement
Most governance programmes fail the same way: a policy document is written, nobody can act on it, and the catalogue is out of date within a quarter. We work the other direction, starting from controls that live in the pipeline.
What we build
A catalogue and lineage generated from the project rather than maintained by hand. Data quality tests on the schedule. Classification at ingestion, with retention rules expressed in code.
For AI systems: risk tiering at design time against the EU AI Act, model cards stored beside the model code, and a logged override path for any decision affecting an individual.
Why it stays current
Because it is produced by the same pipeline runs as everything else. Documentation that requires a human to remember is documentation that will be wrong when it matters.
The test that matters
Someone asks where a reported figure came from, who can see it, and which model touched it. You should be able to answer the same day, without a project.
What we do
The activities this covers
Design
An approach written in plain language, reviewed with your team before anyone writes code.
Delivery
Built in your cloud and your repository, demonstrated weekly against working software.
Validation
Tests and measurement against the success criteria agreed at the start.
Documentation
Runbooks and decision records written during the work, not reconstructed after.
Handover
Training for the people who will own it, scoped from the first week.
What you get
Deliverables, not slideware
- A repository you ownTransformations, infrastructure and CI configuration, commented and reviewed.
- Test suiteTests running on schedule, with alerts routed to a channel your team reads.
- Cost baselineWhat the work costs to run per team, with a monthly budget alert.
- Architecture diagramCurrent state and target state, kept up to date through the engagement.
- RunbooksWhat to do when something fails at 3am, written for someone who did not build it.
- Two weeks of trainingHands-on sessions with the analysts and engineers who take it over.
Related work
Where this service led the engagement
Ridgeline Bank · Financial services
A month-end close that took eleven days
Finance rebuilt the same figures by hand every cycle. We modelled the ledger in the warehouse, defined forty metrics once, and automated the reconciliation.
11 → 3 days to close
Hollis Logistics · Logistics
A warehouse bill that doubled in a year
Nobody could see which queries cost what. We added cost attribution per team, rewrote the twelve worst models, and set a monthly budget alert.
-46% platform cost
Questions
Questions about governance & ai act
Still unsure? Call (216) 123 45678 and ask for whoever is on the desk.
Ask a questionNo. Most engagements improve what you already run. We only recommend migration when the current platform cannot meet a requirement you have already committed to.
You do. Everything is written in your repository, under your cloud account, on open tooling. There is nothing to transfer at the end because it was never ours.
That is the normal arrangement. Your engineers review our pull requests and we review theirs. Teams that stay out of the work rarely take it over successfully.
First working increment usually lands inside three weeks. We demo weekly against running software, not plans.
Tell us which number you cannot trust
Send one paragraph about the report that always starts an argument. A data lead, not a salesperson, replies within one business day.