We design, build and migrate the data and machine-learning platforms that financial services and other regulated businesses run on — and hand them over with the evidence that every number reconciles.
From the pipelines that land your data to the models and agents that act on it, we build systems that hold up under audit, scale and change.
Modern data platforms on Snowflake and Databricks — designed for the volume, auditability and change control that regulated businesses need.
Machine learning and AI that runs in production, not in a notebook — and agents that take real work off your teams’ plates.
Thirteen years of delivery in banks, hedge funds and the public sector taught us that the hard part of data work isn’t building the pipeline — it’s proving it’s right.
Every migration ships with automated reconciliation against the system it replaces. Differences are traced to a named cause — never waved through as noise.
The people who scope your project are the people who build it. No hand-off to a junior bench, no learning on your budget.
Run time and compute cost are requirements, not afterthoughts. Rebuilt pipelines routinely run in a fraction of their legacy time.
Tests, documentation and runbooks come as standard, so your team owns the platform the moment we step back.
Map the data, the systems and the decisions that depend on them. Agree what “correct” means before anything is built.
A target design sized to your team and budget — pragmatic choices over fashionable ones.
Working increments in production early, each with tests and data-quality assertions from the first commit.
Parity evidence against the legacy output, then documentation and a clean handover to your team.
A selection of engagements we can talk about. Much of our work sits behind confidentiality agreements — we’re happy to discuss relevant experience in more detail on a call.
Led the build of the return, risk and exposure engine behind monthly investor reporting — from greenfield to production — replacing a decade-old system that had survived several failed rewrites. Position data modelled with full derivative look-through across hundreds of billions of rows.
OutcomeMonthly numbers released without per-fund manual sign-off, backed by a reconciliation strategy owned by Risk and Fund Accounting.
Streaming ingestion on Databricks for high-volume trade and risk feeds — metadata-driven, schema-evolving and built to absorb thousands of files per trigger — feeding a dbt risk layer on a medallion architecture.
OutcomeDownstream validation rules encoded as automated tests, so outbound submissions passed receiving systems’ gates first time.
Migrated on-premise business data to Azure through fault-tolerant pipelines, and replaced a legacy finance reporting solution with a cloud data warehouse.
OutcomeLess manual reconciliation and more accurate monthly finance reporting across an estate covering around 10% of the NHS.
Tell us about the platform, the migration or the process you want automated. We’ll come back within two working days with honest thoughts on whether — and how — we can help.