Most warehouses fail the same way. Data lands, nothing is modelled, costs climb, and the business keeps working from spreadsheets because nobody trusts what is in the tables. We fix the modelling and governance layer, then attack the cost.
In plain terms
Where teams get stuck
The warehouse bill keeps climbing and nobody can connect it to an outcome.
Data lands in the warehouse but nothing is modelled, so every question needs an engineer.
We have three generations of overlapping tables and nobody knows which ones are safe to use.
Reporting depends on a handful of queries only one person understands.
We pay for a modern stack and still answer questions from spreadsheets.
What we deliver
A review of your warehouse, modelling, ingestion, and spend, with a sequenced plan for what to rebuild, consolidate, or leave alone.
Dimensional and mart modelling in dbt or warehouse-native SQL, with tests and documentation that survive staff turnover.
Query patterns, warehouse sizing, and storage strategy reviewed so spend tracks value instead of habit.
Moving governed data back out into the systems where teams act on it, not just into a dashboard.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
We establish what exists, what it costs, and what is load-bearing.
The layer that turns landed data into something a business team can use.
Making the platform cheaper to run without making it less useful.
Getting data in reliably, with visibility when it does not arrive.
Leaving the warehouse wired into the business and the team able to run it.
The stack we build with
Frameworks, platforms, and infrastructure we implement, integrate, and operate end to end.
What changes
Proof in production
We consolidated four carrier and shipment data sources into a governed warehouse model, analysed all historical shipments, and surfaced more than $300K in annual savings.
Read the warehouse consolidation case study →A unified shipment data model gave full visibility across 3PLs and carriers and cut average shipping cost 15%.
Read the fulfillment data case study →Common questions
Engagements start with a scoped assessment covering architecture, modelling, ingestion, and spend. The assessment produces a sequenced plan and usually identifies enough cost and rework savings to fund the first rebuild.
Usually fix. Most teams we assess do not have a platform problem, they have a modelling and governance problem that a migration would carry across at significant cost. We will tell you when a migration is genuinely warranted, and map it as a bounded sequence.
Often, yes. Warehouse sizing, auto-suspend behaviour, query patterns, and retention policy are the usual causes of spend that outgrows usage. The assessment quantifies what is recoverable before you commit to anything.
Yes, and that is the preferred model. We build the modelling layer alongside your engineers so the standard, the tests, and the documentation live with the people who will extend them.
The first modelled marts are usually usable within weeks, not quarters, because we sequence the work against the questions the business is already asking rather than building the whole schema first.
Yes. The modelling and governance approach is largely platform-independent. We are not tied to one vendor, and the recommendation follows your existing investment and team.
How we work
We review the warehouse, models, ingestion, and spend, then give you a sequenced plan. Cost findings usually fund the work.
We rebuild the modelling layer with tests and documentation, and rationalise the tables that should not exist.
We push governed data into the systems that use it, and hand your team a documented model they can extend.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
