See what AI can handle before you build around it.
We build and test a focused prototype with your examples, then explain what works, what still needs review and whether a full implementation is worth pursuing.
When it helps
When AI could reduce manual work, but you need evidence from your own information before taking it further.
People keep reading and retyping the same kinds of information
Documents, messages or requests need to become usable records. Their wording and format vary, so simple rules do not cover the task.
- Choose one task with a clear, useful output.
- Try AI on representative examples of the incoming information.
- Compare the results with what your team needs from that task.
The demo works, but your real inputs are messier
A few clean examples look promising. Missing details, unusual wording and inconsistent formats could change how much of the work AI can handle.
- Include ordinary, difficult and incomplete examples in the evaluation.
- Check where results remain useful and where they fail.
- Record which inputs still need testing or a different approach.
Checking every result could become another job
Producing an answer is only part of the task. You need to understand the mistakes and the effort of checking or correcting the output.
- Compare outputs against agreed examples and business criteria.
- Try checks and a review approach suited to the task.
- Assess the remaining review effort and processing cost assumptions.
You know what you want to test, but cannot get to it
The idea may already be clear. Building a prototype and checking it properly still competes with the work of running and growing the business.
- Build on your brief, examples and existing findings.
- Take care of the agreed prototype and evaluation work.
- Bring back the results and practical options for what happens next.
A working prototype and evidence for your next decision.
We agree one processing task, representative inputs and what a useful result looks like. You can then see what works, what needs checking and what remains uncertain.
Build and evaluate one focused task.
- Definition of the task, example inputs, required output and evaluation criteria.
- A working prototype for the agreed information-processing task.
- Relevant output checks and a practical way to inspect or correct test results.
- Evaluation using agreed examples, including difficult and incomplete inputs.
- Findings, a demonstration and a handover for the agreed evaluation use.
Results you can inspect, with the limits explained.
- The working prototype and setup notes for the agreed evaluation environment.
- Documented inputs, expected outputs and criteria used to assess the task.
- Test results, observed errors, review effort and processing cost assumptions.
- A recommendation to proceed, refine the approach or stop, with the reasons explained.
Beyond the agreed prototype and evaluation.
Any further work is optional and agreed separately.
- Production deployment, live business actions or a complete operational workflow.
- Additional processing tasks, extensive data preparation or custom model training.
- Ongoing hosting, monitoring, maintenance or further evaluation after handover.
How it works
We lead the build and evaluation. Your team provides examples and helps confirm what a useful result looks like for the business.
Agree the task and examples
We use your brief and representative material to define the input, output and evaluation criteria. We confirm data access, dependencies, price and delivery timing before starting.
Build the prototype
We build the agreed processing task, combining AI with suitable checks. The prototype keeps results available for inspection and shows how selected cases could be reviewed.
Check the results
We evaluate the agreed examples, record errors and examine the checking still needed. We discuss findings with the people who understand the task and note what the sample cannot establish.
Demonstrate and hand over
We walk through the working prototype, results and limitations. You receive the agreed evaluation setup and a recommendation on whether to proceed, refine the approach or stop.
Which information task would you like to test?
An informal 30-minute call to discuss the task, the information involved and whether an AI Information Processing Prototype is the right fit.