Data engineering for AI services.
AI-ready data, first.
Pipelines, warehouses and vector stores that give AI clean, current and permitted data.
The problems we solve first.
Messy data
Duplicates, gaps and conflicting records.
Freshness
Yesterday’s data gives yesterday’s answers.
Governance
Who can use which data, for what.
What you get.
Every engagement includes design, engineering, evaluation, security review and handover from one senior team.
Data pipelines
Ingestion from apps and SaaS tools.
Warehouse
Modelled, documented tables.
Vector stores
Embeddings kept in sync.
Data quality
Tests and alerts on bad data.
Governance
Access, lineage and retention.
Feature store
Reusable ML features.
Data engineering for AI, by industry.
How we ship AI that holds up.
Evaluation comes before scale. You see it working on your data before you commit to the full build.
Discover
Pick the task, define what “good” means and collect sample data.
Prototype
A working version on your real data, shown to real users.
Evaluate
Automated tests for accuracy, latency, cost and safety.
Productionise
Security, scaling, integrations and admin controls.
Monitor
Quality, drift and spend tracked; prompts and models improved.
Three ways to start.
AI sprint
One use case, prototyped on your data and tested with users.
You leave with · Prototype + go/no-goPilot
A production-grade version for one team, with evals and monitoring.
You leave with · Live pilot + ROI dataAI team
A dedicated squad that ships and improves AI across your products.
You leave with · Roadmap deliveredQuestions, answered.
Most clients start with a two-week AI sprint. Pilots and production builds are priced after the sprint, once we know what works on your data.
Ready to try data engineering for ai? Start with a sprint.
Tell us the task. An AI architect replies within one business day with a sprint plan. NDA on request.