Data & AI
Data your analytics and AI can both rely on
We design the platform, pipelines and semantic layer that analytics reads, and the retrieval and evaluation that AI applications need. Access, classification and lineage are designed in from the start, and your data team owns the code.
Services
What this practice covers
Each service has its own page: the problems it addresses, what we set up, what you keep and how the work runs.
Data platform architecture
A lakehouse or warehouse organised into landing, curated and serving zones, with data contracts, access policy and lineage your team owns.
Data engineering and pipelines
Contracted ingestion, tested transformations in dbt or Apache Spark, and lineage at every stage, run from version control through one deployment path.
Analytics and semantic models
A semantic model with governed metric definitions, so board reports and team dashboards in Power BI or Looker show the same numbers.
AI solutions on governed data
Retrieval-based AI applications on your governed data, with an evaluation set that gates each release and monitoring that feeds back into it.
What you keep
What your team keeps when the work ends
Code, pipelines, runbooks and decision records that outlast the engagement, held in your repositories and owned by your team.
Platform zones, storage and access model
Landing, curated and serving zones on the cloud you already run, with storage layout, access model and cost boundaries agreed before the first table lands.
Pipelines as version-controlled code
Ingestion and transformation defined in your repositories, with schema contracts, data tests in CI and idempotent loads, released through one reviewed path.
A semantic layer with agreed metrics
One governed definition of each business metric, with row-level security, so a board report and a team dashboard cannot disagree about the same number.
Evaluation sets that gate AI releases
Questions and expected answers written with your subject experts and run before each release, so the results decide whether a change to prompts, retrieval or model ships.
Data classification and access control
Who can reach which data, under which classification and with what audit trail, designed with the platform, because adding it later means reworking every pipeline and report.
How we work
How the work is sequenced
Short phases, each closed by a review your team signs off, so priorities can change between phases without reopening finished work.
01
Start from a decision
We start from a decision the business wants to make better, then work back to the data it needs. When a report on existing data is enough, we say so before any platform work begins.
02
Design the platform and access
Storage zones, the access and classification model, environment separation and cost boundaries are agreed with your data and security leads, then recorded as decisions your architects sign off.
03
Write the pipelines and models
Ingestion and transformation go into version control with schema contracts and tests, followed by the semantic model the business queries. Your engineers write alongside ours from the first pipeline.
04
Add AI where it helps
Where retrieval or generation genuinely helps, we build it on the governed platform, with the evaluation set agreed first and monitoring in place before anyone relies on the answers.
05
Hand over and operate
Your team takes the pipelines, models, evaluation sets and runbooks, with the reasoning behind the structure written down, so the platform can grow without us.
Next step
Start from the decision you want to improve
Tell us which decision or report matters most and what data sits behind it. We will say whether it needs a platform, a pipeline or neither.