From expert work to a testable AI workflow.
Most AI projects focus on what the model can do. We focus on how it approaches the work.
Observe. Extract. Build. Test.
Observe the work
We start with a real task, process or decision. This may include interviews, examples, historical cases, existing procedures, expert walkthroughs or synthetic test cases.
Extract procedural knowledge
We identify the reasoning patterns behind the work, organized around its decision points. For each one:
- what experienced people do there instead of the obvious default,
- which questions they ask and which evidence they trust,
- the thresholds and settings they actually use,
- which exceptions change the path,
- and when to stop, or when uncertainty should trigger escalation.
Where the evidence runs out, we ask the people who know rather than guess.
Build the reasoning workflow
We turn those patterns into a method and place it inside a process: which evidence each phase may use, what it must produce, who accepts it, and when it escalates to a person.
The method is then implemented for the model it will run on. The same method may be one model call for one model and several specialized stages, agents or tools for another.
The architecture follows the task and the model — not the current AI fashion.
Test it against the work
We compare the system against:
- baseline models,
- existing workflows,
- expert decisions,
- known cases,
- failure scenarios,
- and relevant performance criteria.
We test the whole workflow on cases it was not designed on. Where it fails, we find the decision point and the signal it missed, change one thing, and test again — so every improvement can be traced to a specific change.
The question is not whether the system sounds expert. The question is whether it behaves usefully under realistic conditions.
A role prompt can make AI sound like an expert. That does not mean it behaves like one.
It is easy to ask a model to “act as a senior consultant”, “think like a quality engineer” or “be a skeptical reviewer”. That can change style and sometimes improve performance.
But for important work, the more useful questions are:
- Which reasoning procedures should the system apply?
- Which of them does the base model already perform reliably?
- Which need to be explicitly invoked?
- In what order?
- Under what conditions?
- Which procedures improve performance?
- Which merely change the wording?
- Where should the system stop and hand the decision to a human?
This is where Impersonato differs from generic role prompting or agent orchestration.
What we have learned about what actually changes behavior
- Rules at specific moments, not personalities. Instructions about caution, persistence or when to stop change what a system does when they are tied to a specific situation. Written as a character, a motive or a tone, they mostly change the wording.
- The method as a whole, not a list of tips. A procedure can stop working when its settings, exit rules or the meaning of what one step hands the next are lost — even if every remaining step looks right on its own.
- Conduct, not knowledge. Procedures change what a system does at decision points. They do not give a model knowledge it lacks. Where that is the limit, we say so.
We do not assume that a different role produces better thinking. We test what changes the work.
First prove the reasoning.
Then operationalize it.
Start with a Research & Validation Sprint
Before building a large system, establish whether the proposed reasoning architecture actually improves the work. A sprint can include:
- task definition,
- baseline model behavior,
- expert interviews,
- reasoning procedure extraction,
- prototype workflow,
- synthetic or real test cases,
- comparative evaluation,
- failure analysis,
- recommendation for deployment.
Output: a tested prototype and evidence about whether the approach is worth implementing.
Then integrate where useful
If validated, the workflow can be implemented through:
- API,
- dedicated application,
- existing enterprise systems,
- private cloud,
- VPC,
- or on-premise deployment.
The technology comes last.
Once the reasoning is proven, it runs on a governed runtime: a process contract for each phase, methods compiled for the exact model they run on, case evidence with its origin and permissions, and review gates where the decision belongs to a person. It changes only through new versions and re-testing, never silently.
Have work where the quality of reasoning matters?
Talk to us about a Research & Validation Sprint.