Models that touch revenue, not demos.
A warehouse worth trusting, the contracts that keep it trustworthy, and the two or three models that change a decision someone actually makes.
A pilot that impresses nobody twice.
Most AI work fails on the data underneath it, not on the model. We start with the pipeline, the definitions and the access rules, and only then put a model in front of a decision.
Clean inputs, or a confident guess.
AI & Data — the pipelineWhat an AI and data engagement covers.
Warehouse
A modelled store the business can query, fed from the systems of record rather than from exports.
Data contracts
What each source promises, what breaks when it changes, and the tests that catch it before a report lies.
Enrichment
Firmographic and behavioural signals joined to the account, so segmentation is a query rather than an opinion.
Applied models
Scoring, routing, summarisation and forecasting — put where a person is already making the decision.
Assistants
Retrieval over your own documentation and CRM, scoped to what a role is allowed to see.
Governance
Access, retention and audit written down, so the legal review is a conversation rather than a blocker.
Pipeline first, then the model.
Audit
What data exists, where it is authoritative, and which questions it cannot answer yet.
Pipeline
Ingestion, modelling and tests, so the same query returns the same answer tomorrow.
Apply
The smallest model that changes a decision, shipped into the tool where that decision is made.
Operate
Monitoring, evaluation and a retraining cadence, handed to a named owner.
Put the model where the decision is.
Tell us what you are running
What the system does today, where it breaks, and when it has to work. An engineer reads it — you get an answer inside one business day, not a sequence.