Applications, Data & AI
Modernize applications, unify data and put AI to work where it drives outcomes.
AI is only as good as the application and data estate underneath it. We modernize the systems holding the business back, consolidate the data they produce, and ship AI use cases that have an owner, a baseline and a number attached.
Pilots that never reach production
The barrier to enterprise AI is rarely the model. It is data nobody trusts, applications with no APIs, and pilots with no owner in the business. Value appears when a use case is wired into a real process and measured against how that process performed before.
- Several AI proofs of concept, none in production
- Different teams reporting different numbers
- Core applications with no integration surface
- No agreed way to evaluate model quality
What we do
Application modernization
Decomposition, re-platforming and API-first integration of legacy estates.
Data platforms
Ingestion, modelling, governance and lineage on a single warehouse or lakehouse.
Data quality and governance
Ownership, definitions and quality checks so one metric means one thing.
AI engineering
Retrieval, evaluation, guardrails and human review around assistants and agents.
MLOps
Deployment, monitoring and drift management for models already in production.
Technical debt reduction
Continuous, funded removal of the code and infrastructure that slows every release.
How we engage
- 01
Pick the use case
Shortlist against value, data readiness and process ownership. Anything without a named owner is deferred.
- 02
Prepare the foundation
The specific data products, APIs and access controls the use case needs — not a two-year platform programme.
- 03
Build and evaluate
Iterative delivery with an evaluation set, guardrails and a measured baseline from the current process.
- 04
Scale
Productionize, monitor for drift and reuse the platform components for the next use case.
Common questions
- Do we need a new data platform first?
- No. We build the narrow slice of data the first use cases need, then widen it as demand proves out.
- Which models do you use?
- Whatever fits the task, your data residency rules and your cost envelope. We design so a model can be swapped without a rewrite.
- How is AI risk handled?
- Access control, retrieval boundaries, output evaluation, logging and human review on decisions that carry consequences.
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