Forward-deployed teams. AI-native delivery. Your controls.
We would rather show you working software in your environment than a slide about it. Here is how an engagement runs and how we treat your data.
Forward-deployed engineering
Senior engineers embedded in your environment, shipping against real constraints instead of a static statement of work.
- Delivery pods alongside your product, operations and domain experts
- Daily build-measure-learn cycles on production data and real users
- Direct access to your backlog, sprint ceremonies and release process
- Knowledge transfer and runbooks, so the capability stays when we leave
The AI-native software development lifecycle
Generative and agentic AI used as a real part of every phase, not a copilot bolted on.
- Spec-driven development: AI-assisted requirement breakdown and architecture review before code
- Agentic coding in real codebases, with human review gates at every merge
- Eval-driven QA: generated tests, self-healing suites, regression triage on every commit
- Model provenance logging and rollback controls at every phase gate
Fewer people, moving faster, with routine work automated. We show you the velocity data from the work instead of asking you to take a multiplier on trust.
How an engagement runs
- 01
Talk
A 30-minute call about one real problem.
- 02
Discover
We look at the system with your team and write down what we find.
- 03
Build
An embedded pod ships in your environment against agreed outcomes.
- 04
Hand over
Runbooks, documentation and training. The capability stays with you.
How we handle your data. Plainly.
The questions every enterprise asks an AI and data engineering partner, answered before you have to ask them.
How we work with your data
- Inside your environment (on-premises, hybrid or cloud) under your access controls
- Scoped, least-privilege access, revocable at any time and logged in your systems
- Source code, models and IP produced in the engagement are yours on delivery
- Structured handover: runbooks, documentation and knowledge transfer
AI-specific commitments
- Your data is never used to train models we reuse for other clients
- Enterprise model endpoints with zero-retention terms, or self-hosted models where data cannot leave your network
- Prompt, response and model provenance logs kept in your environment
- Human approval on any agent action that writes to a production system
Start with one real problem.
Tell us about the asset estate or system you are working on. A senior engineer will reply, not a sales team.