Services
Data platforms, and the AI that runs on them.
Eight services in two disciplines. Most engagements start with one and grow from there.
01
Data platforms
The foundation everything else depends on: architecture, pipelines, governance and cost.
Data platform architecture
Lakehouse and warehouse platforms on Google Cloud, AWS, Azure, Databricks and Snowflake, designed around how your organisation actually produces and consumes data. Storage, catalog, security and serving are decided together, not bolted on later.
What you get
- A target architecture your engineers can build without guesswork
- Clear ownership boundaries between ingestion, modelling and serving
- A platform that still makes sense when the third team joins it
Metadata-driven frameworks
Instead of hand-writing every pipeline, we build frameworks that generate ingestion, validation and publishing from configuration. A new source takes hours instead of weeks and behaves like every other.
What you get
- Onboard a new source by adding metadata, not code
- Uniform logging, lineage and error handling across every pipeline
- Fixes reviewed once in the framework and applied everywhere
Pipelines and migrations
ETL and ELT, change data capture, dbt modelling and the awkward moves: SAP and ERP extracts, legacy files, late corrections and whole-platform migrations.
What you get
- Idempotent loads you can rerun without a cleanup script
- Reconciliation built in, so source and target are compared on every run
- Failures that are visible before your users notice them
Governance, quality and cost
Catalogs and access models such as Unity Catalog and its equivalents, data contracts, quality checks and cost visibility, set up so access, lineage and spend stay visible.
What you get
- Governance an auditor can follow without a walkthrough
- Quality gates that stop bad data before it spreads
- Compute sized and scheduled for cost as well as speed
02
Applied & agentic AI
AI that uses your data and your tools, with the controls production needs.
Agentic systems and LLM integration
Agents that use your tools and your data, built on the Claude, OpenAI and Gemini SDKs and the cloud AI services, plus the workflow automation around them.
What you get
- Agents with defined tools, limits and fallbacks
- Integrations your engineers can test and extend
- Manual handoffs replaced where it pays
AI governance
Audit trails, PII controls, cost gates and human approval steps designed in from the start, with documentation that supports EU AI Act obligations.
What you get
- Every agent action traceable after the fact
- Spend capped before it surprises you
- Evidence ready when compliance asks
RAG and evaluation
Retrieval over your own data, and evaluation that measures whether the answers are right, run in CI like any other test.
What you get
- Answers grounded in your own sources
- Evaluation sets that catch regressions
- Quality thresholds that gate releases
AI-assisted engineering and enablement
Agentic coding workflows for your teams, plus the leadership, standards and mentoring that make them stick.
What you get
- Teams shipping faster with review and governance intact
- Written standards and decision records instead of tribal knowledge
- A team that can extend the platform after we leave
Not sure which one you need?
Describe the problem. We will tell you which of these applies, or that none of them does.