You probably need this if…
- Good ideas surface but never get past the “who has time” stage.
- You've bought innovation before and got a demo that never shipped.
- Your competitors are moving on something and you can't tell if it's real.
- Every experiment has to be squeezed past the production backlog.
- You want to try things without touching the system the business depends on.
What's in scope
Opportunity framing
Turning a vague ambition into a testable hypothesis with a success threshold agreed before the work starts.
Rapid prototyping
Working prototypes in weeks, built against real data in an isolated environment — not slideware, and not on production.
Technology evaluation
Structured comparisons of tools, platforms and vendors, with a scored matrix and a written recommendation you can take to a board.
Sandbox environments
Isolated environments with realistic, masked data where experiments can fail safely.
Pilot to production
A defined path from a proven pilot into the production backlog, with the hardening, security review and operational requirements costed up front.
Kill criteria
Every experiment has a stated point at which we stop. Cheap failure is the value of the lab.
How we approach it
- 1
Frame
Hypothesis, success threshold, and a time-box.
- 2
Build
A working prototype against real, masked data.
- 3
Test
Measure against the threshold agreed in step one.
- 4
Decide
Productionise, iterate, or stop. In writing.
- 5
Transfer
Winners go into the delivery backlog, with knowledge handed to your team either way.
Yours at the end of the engagement
- Prototypes and their source code
- Evaluation reports for every technology assessed
- A documented decision record for what was tried and why it was kept or dropped
- The sandbox environment for the next round
Relevant experience
Built a custom B2B fleet-management mobile app on top of Odoo, delivered through a GitHub Actions pipeline (Germany).
Prototyped and productionised a CRM AI agent integrated with Odoo data (Germany).
IoT and connected-operations delivery alongside ERP for industrial clients.
Common questions
How is this different from just adding work to the backlog?
Backlog work is committed and must ship. Lab work is time-boxed and allowed to fail. Mixing the two means the experiments always lose to the deadline.
How is it priced?
As a standing capacity — a small dedicated or shared team on a monthly basis, rather than per experiment. You direct what it works on.
What if nothing works?
Then you've learned, cheaply and quickly, which of your ideas don't survive contact with real data — which is worth more than the money spent finding out during a production rollout.
Who owns the IP?
You do. Everything built in the lab is yours, in your repository, from day one.