Data & AI

Data platforms, analytics and reporting, and an AI and intelligent automation practice that takes use cases from pilot to production.

Platforms and tools: Snowflake, Databricks, Microsoft Fabric, Power BI, Python, dbt

A hand points a stylus at printed charts beside a tablet, laptop and calculator on a desk. — Analysts checking a model's output against the figures planners already trust.

Useful AI starts with data that people trust. We build the platform and the governance first, then pick use cases where a model can be measured against a number the business already tracks.

Our AI and intelligent automation practice takes use cases from pilot to production: evaluation, access controls, monitoring and a named owner on your side before anything goes live.

What we do

Data platform engineering
Lakehouse and warehouse platforms with pipelines, orchestration and cost controls, built to be run by your own team.
Analytics and reporting
Governed metrics and self-service reporting, so two departments stop producing two answers to the same question.
Data quality and governance
Ownership, lineage and quality rules for the data your reporting and models depend on.
Forecasting and machine learning
Demand, risk and maintenance models, measured against a baseline and monitored after release.
Applied generative AI
Assistants and document tools that work over your own content, with access controls, evaluation sets and human review where it matters.
Intelligent automation
Document processing and workflow automation for high-volume, rules-heavy back-office work.

Typical engagements

Client details are anonymised.

Demand-forecasting models reduced fresh-food wastage by 18% across 300 stores, and the team that built them now runs the pipeline.

A national grocery chainFresh-food ordering set by hand from spreadsheets, with write-offs rising during promotions.

A specialty pharma company

Adverse event cases keyed in by hand from several intake channels.

Intake automation and a single case platform cut safety case processing time by 40%.

How it is delivered

Platform builds run as fixed-scope projects. Longer programmes of use cases suit an offshore development centre, with data engineers and data scientists working from one backlog alongside your team.

Fixed-scope projects: Agreed deliverables, timelines and acceptance criteria, with a named delivery manager.
Offshore development centres: Dedicated, ring-fenced offshore teams that work as an extension of yours.

Compare all engagement models

Working hours that overlap

Shared hours: an example day in your local time. The offset differs by client, and we plan for at least four hours when both teams are working every day.