Do we need this before doing anything with AI?

Not for everything. A retrieval system over documents can ship without a warehouse. But if the automation has to reason over transactional data, it inherits every inconsistency you have — so the modelled layer comes first, or the automation will be confidently wrong.

Warehouse, lakehouse, or just Postgres?

Under a few hundred million rows with no heavy analytical workload, Postgres with good modelling is often enough and dramatically cheaper. We will tell you which side of that line you are on rather than defaulting to the expensive answer.

How do you stop it becoming a graveyard of unused tables?

Ownership and usage tracking. Every model has a named owner, and models nothing has queried in ninety days get flagged for deletion. Without that, every platform accumulates until nobody trusts any of it.

Can the same layer serve both analytics and AI retrieval?

That is the design goal. Treating them as separate projects is how a board report and an AI feature end up disagreeing about revenue. Shared definitions, with the retrieval layer carrying lineage back to source rows.

By use case

Data Platform Buildout

One version of the numbers, tested and trusted

Scope this for us Model the savings

Usually owned by

Data
Engineering
Finance
Operations

99.5%
Pipeline success rate: < 15 min
Freshness for AI-serving datasets: 40%
Warehouse spend reduction

The change

What actually differs afterwards

Not a maturity model. The concrete difference in how the work happens.

Today

Three systems give three different revenue figures
Reports built from manual exports each month
A pipeline failed silently and nobody noticed for a fortnight
Nobody can say what a column means without asking a person
Warehouse spend rising with no attribution

Afterwards

One modelled source of truth with tested definitions
Reports and AI features read from the same layer
Pipelines fail loudly and page someone
Every model documented and lineage traceable
Spend attributed per domain with anomaly alerts

The symptom that brings people here

Someone asks a simple question — how many active customers do we have — and gets three answers. Finance has one, the product dashboard has another, and the CRM has a third. Each is defensible. None is authoritative.

That is not a tooling problem. It is a definitions problem, and the platform is where definitions get settled and enforced.

Build order

Model the domain first. Start from the entities the business reasons about — customer, order, subscription, invoice. Pipelines built backwards from a single dashboard become unmaintainable the moment a second consumer appears.

Ingest idempotently. Loads that can be safely re-run. A pipeline you are afraid to re-run is a pipeline that will cause a bad night at some point.

Transform with tests that fail the build. Uniqueness, referential integrity, freshness and business rules asserted in CI. A warning in a channel nobody reads is not a test.

Serve both consumers from the same definitions. Analytics and AI retrieval read the same modelled layer. This is the whole point.

Attribute the cost. Spend per domain, visible to the team that generates it. Making it visible is usually sufficient to fix it.

What we will talk you out of

Buying a warehouse you do not need yet. A great many companies with under ten million rows of transactional data are paying enterprise data-platform prices for something a well-modelled Postgres instance would handle, and the migration cost of moving up later is far lower than the accumulated overspend.

FAQ

Questions we get asked

Let's find out what is actually automatable

Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.

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