Run AI on data that holds up under audit
Enterprise AI stands or falls on the records behind it. We build the data foundation, the controls and the audit trail that let intelligent automation run in production and survive review.
The four objections that stop deployment
The pattern repeats across the estate: a promising pilot, an approval process it cannot satisfy, and a business case that never priced the repair work underneath it.
The pilot ran on a hand-cleaned extract
A curated sample proves the model works. Production runs on live systems with conflicting definitions, missing history and records nobody has reconciled, and the result stops being defensible.
CRM, ERP, billing and support disagree
Each holds a defensible version of the same customer. Until one definition is agreed and enforced, the forecast, the pipeline report and every pricing decision inherit the disagreement.
The output cannot be traced
Risk and legal ask which records produced the answer and who approved the change. Under EU AI Act classification duties, a system nobody can trace is a system nobody signs.
The business case ignored remediation
Data repair, reconciliation and governance work land after the budget is approved. The programme then reads as overrun rather than under-specified, and sponsorship quietly moves elsewhere.
Data as a product, with owners and obligations
Each dataset gets a published shape, a named owner and a commitment it can be held to. Pipelines then enforce what policy documents can only describe.
Data contracts at the boundary
Each producing system commits to one definition, a schema, a refresh cadence and a quality threshold. Breaches are caught where they happen, not three weeks later inside a model output.
Lineage from source to answer
Every field carries its origin, its transformations and its owner. When an auditor asks how a figure was produced, the answer is a record, not a reconstruction.
Governance written into the pipeline
Classification, retention, EU residency and consent are enforced by the platform, so NIS2 or DORA evidence is a query rather than a project. Rules that live only in a PDF are not controls.
Remediation priced before approval
Repair, backfill and reconciliation are scoped, costed and sequenced into the plan the sponsor signs. The work does not disappear, so the programme is funded to absorb it.
Make agents safe enough to act
An agent that reads is a demonstration. An agent that writes to your systems is an operational decision, and it needs the same controls you apply to any other actor.
Least privilege by design
Agents get scoped credentials, a defined set of actions and hard limits on value and volume. Capability is granted deliberately, then reviewed like any other access right.
Human checkpoints where consequence is high
Low-risk actions run unattended. Anything touching price, contract, customer commitment or the ledger stops for named approval, with the reasoning presented to the person signing.
Evaluation before and after release
Every agent ships with a test set, an accepted error rate and monitoring that catches drift. Performance is measured against the business outcome, not model benchmarks.
A decision record for every action
Inputs, retrieved context, the action taken and the approver are logged in one place. Auditability is built in, not reconstructed months later when a review lands.
Book a session on the pilot that stalled
Bring the use case, the data behind it and the objection that stopped it. In one working session we map what the foundation needs, what to repair first, and what production readiness will cost.
Book a working session →