Which service do you need?
Our six service lines cover a data platform from the cloud account up to the AI assistant. This page helps you pick, and shows how the rest fit around them.
Where to start
Pick the situation closest to yours; the first call is there to check the choice.
Data arrives late, breaks quietly, or depends on scripts one person understands.
Start with Data Engineering. Every report and model downstream inherits the failures of its pipelines, so those get fixed first.
The warehouse is on premises, slow or expensive, and every new question needs a ticket.
Start with Data Warehouse & Lakehouse. We design the target platform or migrate the old one in waves, and reconcile the numbers before anything is switched off.
You have data but no trusted reporting. Two dashboards give two answers to the same question.
Start with Data Analytics & BI. We agree each metric’s definition with the person who owns it, then build the semantic layer and dashboards on top.
An AI prototype works in the demo, and nobody can say whether it is good enough to ship.
Start with AI & Machine Learning. We build an evaluation set from your real questions first, so “good enough” becomes a score you agree before release.
The cloud bill grows every month, and nobody is sure who can read which data.
Start with Cloud Data Solutions. Infrastructure goes into code, access goes through your identity provider, and costs are reported by team and workload.
Need engineers more than a project? See IT Outsourcing and the two ways to engage below.
How the services fit on one platform
The rows run up the stack, from the cloud account to AI on top. Each line owns one layer and produces what the next one builds on, so you can start at any layer and add the others when you need them. IT Outsourcing is not a layer: it staffs any of them.
Cloud Data Solutions
Accounts, networks, identity and budgets on AWS, Azure or Google Cloud, defined in Terraform and changed through pull requests.
Produces
- Development, test and production environments
- Roles mapped to your identity provider
Data Engineering
Moves data from databases, SaaS tools, files and event streams onto the platform, on a schedule or as a stream, with tests on every load.
Produces
- Raw data landed and tested
- Run history and alerts
Data Warehouse & Lakehouse
Organises raw data into bronze, silver and gold layers, with modelled tables, a catalogue and access rules per domain.
Produces
- Gold tables with named owners
- Lineage from source to table
Data Analytics & BI
Turns gold tables into agreed metrics, a semantic layer and the dashboards people read every week.
Produces
- Metric definitions in code
- Dashboards with owners
AI & Machine Learning
Uses the same governed data for retrieval assistants, agents and predictive models, evaluated before each release and traced in production.
Produces
- Evaluation results per release
- Traces with cost and latency
Two ways to engage
Both use the same engineers, the same architect review and the same delivery standard. What changes is who sets the scope and who runs the work day to day.
How our engineers join your teamA project team, when you have an outcome to deliver and no spare team to deliver it.
The written plan after the assessment sets the scope. A DATAI lead engineer runs the work and demos it every week, then hands it over or keeps running it with you.
Engineers in your team, when you have a roadmap and need more people or a missing skill.
Our engineers work from your backlog and join your stand-ups, managed by your tech lead. Our architects still review their merge requests, and the arrangement ends on the notice agreed in the contract.
Choosing a service
Not answered here? Ask in the request form.
Our problem spans several service lines. Is that several projects?
Not necessarily. The service lines describe the work; the written plan after the assessment covers whichever lines your problem touches, with one team on it.
Do we need a lakehouse before we can do AI?
Not always. An assistant that answers from your documents can start from the documents and your identity system. Models that predict from business data need clean, modelled tables, so they usually come after pipeline or lakehouse work.
Can you work alongside another vendor or our own platform team?
Yes. We work in your repositories and follow your review and release process. The assessment writes down who owns which part, so two teams are never changing the same thing.
What if we can’t tell which service fits?
Book the call anyway and describe the problem. A senior engineer will tell you which line fits, or tell you that none of them does.
Not sure which line fits?
Describe the problem in a few lines and we will suggest where to start.