Data Platforms
A platform shaped by your questions.
Warehouse or lakehouse, the architecture matters less than this: modeled around the questions your team actually asks, built on your cloud, designed to still make sense in five years.
What we build
- Warehouse and lakehouse design on your cloud
- Data modeling around your business questions, not a generic schema
- Access and governance foundations
- The plumbing that keeps it fast and affordable as it grows
Proof
Oxford Economics
We rebuilt the site, the customer portal, and the way economists publish — the platform that delivers event-driven analysis while it still matters.
Read the storyEnvio360
A multi-tenant data architecture that lets an optimisation platform read the TMS each customer already runs — one platform, every tenant's data kept apart.
Read the story- Multi-tenant · Reads the TMS you run
When teams come to us
- Five tools, each with its own version of the truth
- Analysts blocked behind an engineering queue
- A platform bill growing faster than platform usage
The architecture and design choices that sophilabs made at the outset really paid off handsomely.
The five-tools problem
Every company we meet at this stage has the same shape of problem, and it never looks like a data problem from the inside. Finance has a number, sales has a number, the dashboard has a third, and the meeting is spent deciding which one is right instead of what to do about it. Each tool is correct by its own definition. The definitions were never agreed, because nobody was ever asked to agree them — each system was bought to answer its own question, and the questions overlapped.
A platform does not fix that by moving the data somewhere new. It fixes it by making the definition the thing that lives in one place: revenue computed once, from one source, with an owner, and read by every screen. The warehouse or the lakehouse is where that happens; the choice between them is a trade-off about workloads and cost, not the decision that matters. The decision that matters is the one about the questions, and it is the one most platform projects skip on the way to choosing a vendor.
How we build it
Question inventory
what the business actually asks
Model around the questions
not a generic schema
Build on your cloud
with the plumbing to stay fast and affordable
Govern and evolve
access, definitions, growth
What “shaped by your questions” means
The questions come first
before a schema exists, an inventory of what the business actually asks: the weekly report, the board number, the alert nobody set up yet. The model is built to answer those, and it is judged by whether it does.
One definition per number
"revenue" means one thing, defined once, computed once. Five tools with five versions of the truth is the problem most platforms are built to solve and then quietly recreate.
Your cloud, your bill
built where your data already is, with the plumbing — partitioning, caching, retention — that keeps the bill growing with usage rather than ahead of it.
Made to change
the questions will change. The model has to absorb a new one without a rebuild, and the governance has to say who owns each definition when it does.
The architecture decision that matters most is the one about the questions, and it is the one most platforms skip.
Works well with
FAQ
Warehouse or lakehouse?
A trade-off, not a religion. Your workloads decide.
Migrate or rebuild?
Usually evolve: keep what answers well, replace what doesn't.
Which vendor should we pick?
The one your team can run. We design for your case — we don't sell licenses.
Is this a prerequisite for AI?
Often, and not in the way vendors mean. AI needs the data it will use to be reliable and reachable; it does not need everything modelled first. A platform shaped around the questions that matter is usually also the platform that makes the first AI use case possible — the two are one investment when they are scoped together.
How long until we see something?
The question inventory takes a week or two. From there the first questions get answered on the new platform within the first month, and the rest follow in value order — the platform is used while it is being built, not after.
Who runs it once it's built?
Your team, if you want, and it is designed for that: documented, on your cloud, on tools your team can operate. If you would rather we keep evolving it, that is a monthly engagement priced on output.
What if our questions are still changing?
They will keep changing, and the platform has to absorb that without a rebuild. Modelling around the questions means the model has a place for a new one; governance means someone owns the definition when it arrives. A platform that only answers the questions it was built with is a report, not a platform.