Numbers that disagree
Different functions report different figures because each built its own extract years ago.
Industry
Public sector
Transparent models, published methodology, and procurement documentation that survives scrutiny.
Published figures invite scrutiny
That is the point, and it changes how the work is built. A number that cannot be traced to its source will eventually be challenged, and the response time is part of the service.
How we work here
Methodology written for publication, not for an internal wiki. Lineage from source system to published figure. Models that can be explained in a committee without a data scientist present.
Procurement documentation is produced as we go, because reconstructing it afterwards is slower and reads like it.
What tends to pay first
Automating the datasets you are already obliged to publish. The effort is currently manual and recurring, so the saving repeats every cycle and the quality improves at the same time.
Challenges
What we hear from data teams here
Models nobody can explain
A model works, but the documentation needed for review was never written down.
Slow regulatory reporting
Each submission is a manual effort because lineage lives in people, not in the platform.
Regulation
What applies, and what we do about it
| Regulation | What it asks for | What we build |
|---|---|---|
| GDPR | Lawful basis, minimisation and subject rights | Classification at ingestion, retention rules in code, subject access queries that run in minutes |
| EU AI Act | Risk tiering, documentation and human oversight | Risk assessment at design time, model cards, and a logged override path for every decision |
| Sector rules | Accurate, complete and timely reporting | End-to-end lineage from source system to reported figure, with quality tests at each hop |
Use cases
Where the return usually shows up first
Faster reporting cycle
Automated reconciliation and a defined metric layer. Typically cuts the cycle by half.
Predictive models
Documented models with challenger comparison and drift monitoring in production.
Anomaly detection
Near-real-time scoring with a review queue and a feedback loop into retraining.
Customer and demand insight
Behaviour modelled across products rather than one product line at a time.
Audit readiness
Submission packs generated from the warehouse with lineage attached.
Platform cost control
Query cost attributed per team, with budgets and alerting.
Data sources
Systems we usually find on day one
Core systems
Transactions, records, master data
CRM
Customers, interactions, cases
Operational telemetry
Events, sensors, logs
Finance
Postings, hierarchies, currency
Questions
Questions from public sector
Still unsure? Call (216) 123 45678 and ask for whoever is on the desk.
Ask a questionYes. We work in your tenancy under your access controls, with named individuals vetted to your standard. We do not move data out for convenience.
Our governance team writes documentation to the standard second-line review expects, and has sat on the other side of those reviews.
Delivery teams can be restricted to a region, and all storage and compute stay in the region you specify. We publish our subprocessors.
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.