Service 03 of 04
Data & AI
AI earns its place when it removes work or improves a decision — not when it is added to a roadmap. We start from the task you want changed, get the data underneath it trustworthy, and then apply the smallest thing that works.
What this covers
Capabilities inside Data & AI.
- AI integration
- Workflow automation
- LLM and RAG systems
- Data pipelines and ETL
- Warehouse modelling
- Analytics and reporting
- Document intelligence
Artefacts, not status updates.
- Data pipeline with lineage and quality checks
- Evaluation harness, so model changes are measurable
- Human review path for anything consequential
- Cost-per-operation model before you commit
- Fallback behaviour for when a model is wrong
Outcomes we will stand behind.
- 01Manual steps removed from a real workflow
- 02Decisions backed by numbers that reconcile
- 03A measurable baseline instead of a demo
We deliberately do not publish volume metrics or retention percentages. What we publish instead is the architecture and the reasoning behind it, which is the part you can actually assess.
Seven phases, 21 artefacts.
Data & AI runs through the same seven phases as everything else we build, and no phase ends with a status update.
Read the full processWhere this shows up
Data & AI, against a real problem.
Reference architectures
Other pillars
Most engagements cross more than one.
A custom build usually needs somewhere to run and someone to keep it running. The pillars are separated for clarity, not because they get sold in isolation.
Start here
Tell us the problem.
We'll be honest about the fit.
A first conversation costs nothing and is useful even if you go elsewhere — you'll leave with an architecture opinion and a realistic sense of scope.
- Reply within 24 hours
- No sales sequence