Warehouse or lakehouse build
A modelled central store with defined grain and documented definitions, rather than a copy of every source system.
AI Capabilities & Services
Data Engineering & Big Data
Scalable pipelines, lakes, and warehouses that make your data AI-ready.
The problem
Two departments bring two revenue figures to the same meeting and both are defensible, because each was built from a different extract with different filters and a different definition of the month. The reporting problem is really a plumbing problem: data arrives late, breaks silently, and nobody can say which number is the one of record. Every analytics and AI project downstream inherits it.
Who this is for
- Data and BI leadsPipelines that fail loudly instead of delivering yesterday's numbers quietly
- Finance and operationsOne definition of revenue, margin and month that survives being questioned
- IT and platform ownersA platform that can be operated by the team who has to carry the pager
What people use it for
Migration off a legacy platform
Moving to a modern warehouse with reconciliation at every step, so the new numbers can be proved equal to the old ones.
Streaming and change data capture
Where a daily batch is too slow to act on — operational dashboards, alerting, or feeding a live model.
Data quality and observability
Tests, freshness checks and lineage, so a break is found by the pipeline rather than by a director in a meeting.
What it needs to work
- Your source systems — ERP, CRM, operational databases, files, APIs, devices
- Agreement on the definitions that matter, which is a business decision rather than a technical one
- Your cloud or on-premises target, and any constraint on where data may reside
- Who owns each domain, because pipelines without owners rot
How it works
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Ingest
Batch or streaming, with change data capture where the source supports it and full extracts where it does not.
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Land and version
Raw data is landed unchanged and kept, so any transformation can be re-run and any figure re-derived from source.
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Transform to a model
Cleaning and modelling into defined grains, with the business definitions written into the code rather than into a document nobody reads.
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Test before publishing
Row counts, uniqueness, referential checks and freshness run before anything reaches a dashboard. A failed test stops the publish.
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Serve
To BI tools, applications and models from one modelled layer, so everyone is reading the same definition.
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Observe
Freshness, volume and schema drift are monitored, with alerts to the owning team and lineage to trace what a break affects.
How we deliver it
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Source and definition audit
What exists, what it means, and where two systems disagree. The disagreements are usually the real finding.
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Architecture and target
Chosen against your constraints — residency, existing skills, budget and what your team can operate — not against a reference diagram.
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Build one domain end to end
One subject area from source to dashboard, with tests and reconciliation, before the second is started.
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Extend and hand over
Further domains on the established pattern, with your team building the later ones and us reviewing.
Where it runs
- Cloud warehouse or lakehouse on your own tenancy
- On-premises where residency or policy requires it
- Hybrid, with sensitive domains held locally and the rest in cloud
Security and governance
- Lineage from every published figure back to its source rows
- Definitions live in version control and change through review
- Access by role at the modelled layer, with sensitive fields masked
- Retention and residency configured per domain rather than globally
Timeline
One domain end to end is the unit worth planning around, and it is usually weeks rather than months once definitions are agreed. Agreeing the definitions is the part that takes as long as it takes, because it is a conversation between departments rather than an engineering task — and a platform built before that conversation just industrialises the disagreement.
What you receive
- A modelled warehouse or lakehouse with documented grains and definitions
- Pipelines under version control, with tests that block a bad publish
- Observability: freshness, volume, schema drift and lineage
- Reconciliation evidence where numbers moved from an old platform
- Runbooks and training for the team who will operate it
Related work
Published projects where we did this.
What this does not do
Engineering cannot decide what revenue means. Where two departments disagree, the platform will faithfully produce both numbers until somebody chooses — and that choice is yours. Nor does a warehouse fix a source system that records the wrong thing; it only makes the problem visible sooner, which is worth a great deal but is not the same as solving it.
Questions we are asked
Do we have to move to the cloud?
No. The pattern — land raw, transform to a model, test before publishing, observe — works on-premises too. Cloud changes the economics and the elasticity, not the architecture.
How do we know the migrated numbers are right?
Reconciliation at every step, against the old platform, for the periods you care about — and where a figure legitimately changes because a definition was wrong before, that difference is documented rather than smoothed away.
Can our team run it afterwards?
That is the point of building one domain first and having your team build the next. If the platform needs us to keep it alive, we have built the wrong platform.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
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Every InsAI product runs on the same four-stage backbone.
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Forecasting, detection, optimization
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From the floor to the boardroom
Data Engineering & Big Data
Scalable pipelines, lakes, and warehouses that make your data AI-ready.