Technology
Six systems. One team builds all of them.
Most firms own one band of this stack and subcontract or hand-wave the rest. The seams between those bands are where enterprise AI actually breaks. These are the systems we build, in your environment, under your governance — and every one of them is designed to be handed over.
Data in motion, under contract.
Batch, stream and API. The least glamorous system on this page, and the one that decides whether anything above it can be trusted.
Most enterprise AI runs on an extract someone produced by hand, once, six months ago.
The pilot works because the data was curated for it. Production doesn't, because nothing keeps it that way. By the time anyone notices, the model has been quietly wrong for a quarter.
The pipelines and contracts that make every layer above this one trustworthy — with the same guarantees regardless of how the data arrives, and with failure surfaced at the edge rather than discovered downstream.
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Contract-first pipelines
Schemas are enforced at the boundary. An upstream change fails loudly where it happened, instead of silently poisoning a model three layers up.
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One definition, batch or stream
A record means the same thing whether it lands nightly or in milliseconds. You maintain one version of the truth, not two that drift.
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Replayable by default
Idempotent loads with full history. Reprocessing three years of data is a routine operation, not an incident report.
Storage that compounds.
The warehouse or lakehouse, modelled so the tenth use case costs a fraction of the first — and so each one leaves the next team better off than it found them.
Storage is cheap. Storage many teams can query correctly, under load, without surprising the CFO, is not.
Most warehouses are a graveyard of one-off tables built for a project that ended. Every new team starts from zero, and the cost of the eleventh use case is identical to the first.
A foundation where use cases inherit from each other rather than competing. Conformed entities, shared facts, deliberate cost design, and environments that behave the same from a laptop to production.
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Modelled for reuse
Conformed dimensions and shared facts. The fourth team to arrive inherits the work instead of rebuilding the same six joins from scratch.
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Cost as a design constraint
Partitioning, clustering and materialisation are decided at design time — not discovered on an invoice at the end of the quarter.
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Environments that match
Dev, staging and production built from the same code. "It worked in staging" stops being an acceptable explanation.
One definition of the number.
The metrics and entity layer where the business agrees once what "active customer" means — and every dashboard, model and agent inherits it.
Three teams, three revenue figures, and a standing monthly meeting to reconcile them.
Ambiguity at this layer doesn't stay here. Every AI system built on top inherits it and multiplies it, and the first serious question from an auditor or a regulator is the one nobody can answer quickly.
A governed definition layer with lineage back to source, serving humans and machines from the same place — so your board and your agents are never looking at different numbers.
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Metrics as code
Definitions live in version control and are reviewed like software, because that is exactly what they are.
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Lineage end to end
Any figure on any screen traces back to the rows that produced it, without a two-day archaeology exercise.
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A layer a model can read
The same semantics serve the dashboard and the agent. Retrieval is grounded in governed definitions, not in whatever a model inferred from a column name.
Models scoped to a decision.
Forecasting, extraction, ranking, retrieval and agentic workflows — each scoped to a decision someone is accountable for, with the authority boundaries written down.
The interesting question is almost never which model. It is what the system does when the model is wrong.
Capable models are now commodity. What isn't commodity is the composition around them: what gets retrieved, what the system is permitted to do unsupervised, and who is accountable when it acts.
The system around the model. We explore the compositions that actually fit the task rather than the first architecture that demos well, and we write down the authority boundary before anything reaches a user.
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The simplest thing that works
A gradient-boosted tree that ships this quarter beats a fine-tune that impresses in review and never reaches a user.
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Retrieval grounded in your corpus
Answers cite the document and the passage they came from, so a subject-matter expert can check the work in seconds rather than trusting it.
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Agents with defined authority
Explicit scope for what a system may do alone, what it must propose for review, and where a named human signs.
Proof before production.
The test set, the regression gates and the observability that turn a convincing demo into something you can defend to a risk committee.
Most pilots have no failing test to point at — which is exactly why nobody can say when they are finished.
Without a harness, improvement is a matter of opinion and regression is invisible. Systems don't announce the day they stop working; they just quietly get worse while everyone assumes the launch metrics still hold.
A harness your team owns: real cases, gates that block bad changes in CI, and a loop that turns live production outcomes back into test cases — so the system improves on evidence rather than on argument.
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A test set that reflects reality
Built with your experts from real cases, deliberately including the awkward ones. Where no suite exists, we generate a realistic evaluation set from the task itself and have your experts correct it.
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Regression gates in CI
No change ships that improves one behaviour while quietly degrading four others. The pipeline blocks it before a human has to notice.
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A closed loop from production
Drift, latency, cost and quality on one board — and live outcomes fed back as new cases, so improvement is a property of the system rather than a project someone has to fund.
Auditable by design. Then yours.
Access, lineage, audit and handover. Not a layer on top — a constraint that shapes all five systems beneath it, from the first sprint.
Governance added at the end is the most expensive possible way to discover the architecture was wrong.
In regulated sectors this is the difference between a system your board defends and one they quietly retire. The question is never whether it can be explained — it is whether it can be explained in the time you have.
Control designed in from the first sprint, and an exit designed in with it. The architecture assumes we leave, because a system only your vendor can operate is a liability you are paying to keep.
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Access as code
Roles and row-level policy in version control. Reviewable, revocable, and provable months after the person who granted it has left.
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An answer ready before the question
Every output traceable to the inputs, the model version and the policy in force when it was produced. When a regulator asks, the answer is already on file.
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Built to be handed over
Runbooks, documentation and your engineers trained on it. We are structured to leave, and the architecture assumes it from day one.