Data Services
AI is only as good as the data under it.
We build the pipelines, platforms, and quality your AI systems depend on — and make the data you already have usable for what you want to build next.
The sprint includes technical feasibility and data requirements — you'll know exactly where your data stands
What we build
Data Engineering
Pipelines and integrations that move your data reliably — batch and real time — from the systems you run to the places it creates value.
Data Platforms
Warehouses, lakehouses, and the plumbing around them — designed for the questions your team actually asks.
Data Readiness for AI
The gap between “we have data” and “AI can use it”: quality, structure, access, and retrieval where it earns its place.
Analytics & BI
Dashboards and metrics your team trusts — one version of the truth, wired to decisions.
MLOps
Deployment, monitoring, evaluation, and retraining — the operations layer that keeps models honest in production.
Where AI pilots stall
Every stalled AI pilot has the same autopsy: the data wasn't ready. The mistake we see most is treating readiness as a project that finishes before AI begins: clean everything, structure everything, then start. It never finishes. Readiness is relative to a use case. An assistant that answers questions from your documentation needs those documents indexed and current, and nothing else. A model that predicts churn needs one table, joined, labelled and refreshed — and nothing else.
Readiness is not a property of a database. It is the answer to four questions, asked about one use case at a time. Can the system that will use the data actually reach it, under the permissions the data already has? Is it true? Does it have the shape the use case needs? Is there a way from where the data is born to where the model reads it that runs without a person? Most stalled pilots fail one of the four. Almost none fail all four — which is why the fix is usually smaller than the fear.
From the systems you already run
Systems
Connectors to the systems you actually run: databases, ERPs, CRMs, APIs. Old ERPs and odd APIs are our normal case, not the exception.
Pipeline
Data that arrives complete, on time, every time. A pipeline built to bet on knows what a complete run looks like and says so when it isn't — before anyone downstream reads the result.
Platform
The definition is the thing that lives in one place: revenue computed once, from one source, with an owner, and read by every screen. Warehouse or lakehouse is a trade-off about workloads and cost, not the decision that matters.
Quality
Is it true? Duplicates, stale records, fields that mean different things in different systems. A model trained on it will be confidently wrong in exactly the ways the data is.
Your AI
The pilot proves the model can work. Operations is proving, every week, that it still does: deployment, monitoring, evaluation, retraining.
Proof
Data platform engineering for Oxford Economics — architecture decisions that held up as the platform grew. We rebuilt the site, the customer portal, and the way economists publish — the platform that delivers event-driven analysis while it still matters. For Envio 360, 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 →The architecture and design choices that sophilabs made at the outset really paid off handsomely.
How engagements run
Discovery Sprint → fixed-price work in value order: the subset of data that unblocks the first use case, made reliable first, verified with the AI itself → built to hand over — documentation, alerts, runbooks — or, for teams that keep building, a monthly engagement priced on output, not hours.