Task coverage
Check representative cases, including common variations and edge cases.
Engineering approach
How we move from an AI idea to a verifiable result.
Define the task and baseline
We start with the current process, the data it uses and the outcome the organization needs. Together, we agree how to measure the starting point and what success means.
- The task and the people involved
- The data and documents available for the work
- The outcome the organization needs
- The current time, quality or error rate
- Measures that will show whether the solution helps
A staged delivery process
Each stage has a clear purpose and an explicit next-step decision. A pilot and a deployment are planned separately, based on measured results and operating requirements.
-
Discover
Describe the real process, the data involved and what would count as success. Check whether AI is needed at all.
-
Build
Build a working solution for one task.
-
Demonstrate
Run it in the laboratory environment on permitted sample data, and show what it does and where it fails.
-
Measure
Compare agreed measures with the starting process. Document the results and the limitations.
-
Pilot
A separate decision, with its own scope, access, error handling and responsibilities.
-
Deploy
Only if justified: reproduce an equivalent architecture in the organization’s own environment. A future stage that needs a separate assessment.
Not every project passes through every stage. A project may end after any of them.
How we verify a solution
We check the solution against agreed tasks and realistic examples, then report both what works and where it falls short.
Quality and errors
Compare outputs with expected results and record incorrect, incomplete or uncertain answers.
Operational fit
Review handling time, review effort, data boundaries and the conditions needed to use the solution.
Human review stays in the process
Where a decision affects people, money, safety or official records, define who reviews the result and who is accountable before a pilot begins.
A clear record of the work
The project record makes the scope, evidence and recommendation understandable to the people who must decide what happens next.
- Agreed task scope and success measures
- Data used and processing boundaries
- Test cases and measured results
- Errors, edge cases and known limitations
- Recommendation: continue, revise or stop
Selected results from our projects
These project pages document outcomes from specific completed runs and the context behind them.
Migrating tasks from Jira to GitHub Projects
Moving to a new system — without transferring hundreds of tasks by hand.
Almost 500 tasks, comments, attachments and key metadata transferred in about 4 hours
View projectLocal transcription of large audio files
Hours of recordings turned into ready text — without cloud service limits.
About 60 hours of lectures processed in about 5 hours on a GPU
View projectA decision based on evidence
The next step follows the agreed measures and observed limitations. Moving forward is never automatic.
Continue
The result meets the agreed measures and a defined next stage is justified.
Revise
The evidence points to a change in scope, workflow, data or technical approach before another review.
Stop
The measured benefit, quality or operating fit does not justify further work.
What a pilot requires
A pilot is a separate, bounded engagement. Before it starts, the organization and the laboratory agree on:
- The users, tasks and duration in scope
- Approved data, access and retention rules
- Review, escalation and error-handling responsibilities
- Success measures and the decision date
- What happens to data and access when the pilot ends
Start with one process.
Describe what is currently done manually, what data is involved and what result you would like to achieve. You do not need a technical specification or a chosen model to begin.
Please do not share trade secrets, source code, or sensitive personal data before an NDA (non-disclosure agreement) is signed.