Overview
Data work that holds up under audit.
We design, build and run the data platforms, reporting and AI systems that banks, insurers and fintechs depend on — with the lineage, controls and documentation a regulator will ask for.
No number leaves the layer without a source you can name.
Capability
Four things we are asked for, usually together.
Most engagements start with one of these and pull in the others. We staff small teams that stay with a system past go-live.
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Data management
Platforms and the governance around them
Warehouses, pipelines and the rules that keep them trustworthy.
- Architecture on cloud or on-premise
- Ingestion from core, payments and policy systems
- Quality rules, contracts and monitoring
- Catalog, lineage and access control
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Business analysis
Turning a rule into something buildable
We sit between the business, the regulator and the engineers.
- Process mapping and requirements
- Report and KPI definitions
- Data models reviewed with the business
- Specifications engineers can estimate
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Delivery management
Someone accountable for the date
Planning, vendors and cutovers — held together in one place.
- Delivery plans and vendor coordination
- Migration and cutover runbooks
- Risk, dependency and scope tracking
- Reporting a steering committee can read
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AI solutions
Models that reach production and stay there
Screened against your data first, monitored after launch.
- Use-case screening on real data
- Document and text processing
- Forecasting, scoring, anomaly detection
- Drift checks and model documentation
Sector
Built for institutions that have to explain themselves.
Financial services is where our work concentrates: long-lived core systems, data that carries legal weight, and a supervisor who can ask for the workings.
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Banking
Core, credit and supervisory reporting
Extracting from a core banking system without disturbing it, then producing credit, liquidity and supervisory figures that reconcile to the ledger.
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Insurance
Policy, claims and actuarial data
Joining policy administration, claims and finance into one model, so reserving and pricing work from the same numbers the accounts do.
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Capital markets
Positions, valuations and reference data
Instrument and counterparty reference data kept clean, and end-of-day positions that arrive on time and match.
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Fintech
Scale without losing the audit trail
Growing teams that need reporting, controls and model governance in place before their first regulatory review, not after it.
Engagement
Three stages, and you can stop after any of them.
Each stage ends with something you own and can hand to another supplier. No stage depends on booking the next one.
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01 / Assess
Find out what actually breaks
Two to four weeks with your systems and the people who use them. We map the data you hold, where it stops being trustworthy, and what the gaps cost you.
Ends with: a costed plan and a priority order
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02 / Build
Ship it with your team, not beside it
A joint team on your tooling and your review process. We work in increments that go live, so value arrives before the programme ends.
Ends with: a running system and its documentation
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03 / Run
Keep it healthy, then hand it over
Monitoring, support and change requests under an agreed response time — plus a handover plan from the first day, whenever you want to take it in-house.
Ends with: your team running it unaided
Next step
Tell us what's breaking.
A late report, a migration nobody wants to own, a model stuck in a pilot. Send the short version — we will tell you whether it is work we should take.