Most AI pilots never reach production.
The demo works on a cleaned-up sample. Connected to live data, real permissions and real volumes, it stalls, and the budget keeps running.
- A pilot that works in the demo but not with live data
- AI spend rising while the business waits for results
- No in-house team to build and run it
- Security and governance questions raised only at the end
Three steps, one accountable team.
We agree the use case, the data it needs and what “live” means, with measurable success criteria.
- Use case and success criteria
- Data sources and access
- Security and governance requirements
We build the part of the lakehouse the use case needs and the agent on top, from our blueprints, on your platform.
- Connectors and lakehouse layer
- The agent, with evaluation and human review
- Production deployment and monitoring
You get the running system, the code we wrote for you and the documentation. We can keep operating it for you.
- Custom code in your repositories
- Runbooks and documentation
- Option to continue with Managed
Four two-week sprints, each with a deliverable.
Every two weeks you see working results on your own data, and decide with us what comes next.
Kick off and connect
We agree the use case and what “live” means, get access to your data and platform, and clear security.
Your data connected and checked
Build on real data
We build the part of the lakehouse the use case needs and a first version of the agent on top.
A working agent on your data
Test and harden
We evaluate answer quality, set access rules and monitoring, and review the results with your team.
An evaluated system, ready for release
Go live
We release to production, hand over runbooks and documentation, and watch the first weeks closely.
Your first use case in production
After launch: run it yourself, extend it with a Build, or have us operate it with Managed.
From kickoff to your first use case in production.
One use case, agreed success criteria and an agreed go-live date.
Built on your platform. Your data never leaves your account.
Results in production
Questions we hear
What counts as “live”?+
A use case running on real data, used by the people it is built for, with monitoring in place. We agree the exact criteria at the start.
What do you need from us?+
A use case owner, access to the relevant data and platform accounts, and a technical contact for security and deployment.
Which platforms do you build on?+
AWS, Snowflake, Databricks, Google Cloud or on-premises, with Claude or open-source models.
Where does it run, and who controls it?+
Everything we build for you is yours: pipelines, data models, infrastructure code and documentation, in your own cloud account and repositories. Our agents and accelerators come with a licence that keeps working even if you stop working with us. If you need full source access, we offer that too.
What happens after the Sprint?+
You can run it yourself, extend it with a Build, or have us operate it with Managed.
Does every agent need a lakehouse?+
No. If the agent works on live records in one or two systems, such as tickets, orders or code, we connect it directly through MCP or the system’s API. A governed data layer is needed when it has to answer questions across systems or history, so its numbers match your reports. Many use cases combine both.
Put your first use case into production.
Tell us the use case and the platform you run.