Feeds arrive in different shapes, at different times, from providers with different ideas of what a runner, a market or a settled transaction is. Reconciling them is the actual work. Pricing, settlement, reporting and regulatory returns all inherit whatever was or was not fixed at ingest.
This service line connects data engineering to AI implementation: forecasting, computer vision, text classification and decision support, backed by pipelines that can be monitored and replayed.
For delivered examples and an integration path, read AI in your existing systems.
Ingest, normalise, reconcile
We land raw source data before transforming it, so a provider changing a field on a Friday afternoon is a diagnosable event rather than a mystery in a report on Monday. Normalisation maps every source onto one internal model with stable identifiers. Reconciliation handles the messy part: duplicate records, corrections that arrive after you have already acted on the original, results that get amended, and adjustments back-dated across a period you have already reported on.
Pipelines are built to be replayed. If a provider re-issues three days of files, that is a re-run against a known position, not a week of manual patching.
Two kinds of reader, one lineage
Trading and operations need current state and can live with an estimate that is seconds old. Finance and compliance need numbers that reconcile exactly and can live with a lag. We build both off the same lineage and keep them as separate serving layers, because collapsing them into one “single view” is how organisations end up with a dashboard nobody trusts and a reconciliation nobody can reproduce.
Models, and when to skip them
We build and deploy machine-learning models for ranking, forecasting and anomaly detection on transaction and account behaviour. We also say when not to. If a rule expresses the intent, the rule is cheaper to operate, easier to change and possible to explain to a regulator. A model that cannot be explained is a compliance problem waiting for its moment.
Governance is a deliverable, not a phase
Ownership per dataset, lineage at field level, retention rules, access control, personal-data handling and a change process for schemas other teams depend on. Written down, in the repository, not in a slide deck.
When this is the wrong engagement
If the problem is one broken report, you do not need a data platform, and we will tell you that in the first conversation. We also will not put analytics on top of a feed that has never been reconciled. That does not produce insight, it produces confident wrong numbers with a chart attached.
Where the output has to stay correct overnight and on weekends, this service line hands over to the 24×7 Operations Desk with the monitoring and runbooks already written.