Engineers inside your team, until it runs.
Four ways to work with us, from a short strategy sprint to engineers embedded in your team. Most clients start small and grow the engagement once the first system is live.
Featured engagement
Forward Deployed Engineers
Grolytix engineers join your team, work in your systems and on your problems, and stay until the solution is live and your people can run it. No handoff gap between the people who design it and the people who ship it.
Best for
- AI or data work that touches several internal systems and teams
- Organisations without an in-house AI or data engineering team yet
- Projects where requirements will change as you learn
You get
- Working software in production
- Code in your repositories
- Documentation and runbooks
- A team that knows how it works
How it works
- Step 1Engineers join your standups, chat and repositories and work to your security policies
- Step 2Work is planned with your product owner in short cycles
- Step 3Your team pairs with ours so knowledge stays when we leave
- Step 4The engagement ends when the system is in production and owned by your team
AI Strategy Sprint
A ranked roadmap and a working prototype, in a fixed scope.
A short, fixed-scope engagement for leadership teams who want to move from "we should do something with AI" to a specific plan. It ends with a prioritised roadmap and a working prototype of the top use case on your own data.
Best for
- Leadership teams starting their AI plan
- Companies with many AI ideas and no way to choose
- Boards asking for a credible AI roadmap
You get
- Readiness assessment
- Prioritised roadmap
- Working prototype
- Leadership presentation
Build and Handover
A defined system, delivered, documented and handed to your team.
Fixed-scope delivery of a specific system: an assistant, a pipeline, a dashboard, an app. We agree the scope and acceptance criteria up front, build it, and transfer it to your team with documentation and training.
Best for
- Well-defined projects
- Teams who will own and run the system themselves
- Budget holders who need a fixed scope
You get
- The agreed system, in production
- Source code and infrastructure as code
- Documentation and training
- A support window after handover
Managed AI Ops
Ongoing monitoring, evaluation and improvement of your AI systems.
AI systems need care after launch: models change, documents go stale, costs drift and users find new edge cases. We monitor, evaluate and improve your AI and data systems on an ongoing basis.
Best for
- AI systems already in production
- Teams without dedicated AI operations staff
- Systems where quality and cost need regular attention
You get
- Dashboards and alerts
- Monthly quality and cost report
- Fixes and improvements
- A named contact
Not sure which model fits?
Tell us what you are trying to do. We will suggest the smallest engagement that gets you to a working system.