A working prototype
It runs on real examples you provide. It is software you can use, not slides about software.
Services / Start
Your top use case.
A working prototype in three business days.
Choose the AI use case you most want to test. In three business days we build a working proof of concept on realistic inputs, so you can judge it with your own eyes before you commit to more.
- Best when
- You can name the use case and want proof before a bigger investment.
- You bring
- One well-chosen use case, sample inputs and someone who knows what good looks like.
- You leave with
- A working prototype, what we learned and a recommendation: proceed, change or stop.
- Shape
- Three business days, with scope agreed up front.
You might be here if
Does any of this
sound familiar?
- You have seen impressive demos but do not know whether AI will work on your documents and your data.
- Leadership wants evidence, not another presentation.
- A workshop or an internal review produced a promising top candidate.
- You are comparing vendors and want something real to test them against.
Why three days
Test the idea
on your own inputs.
A prototype on your own inputs answers questions a pitch cannot. Does it read your documents correctly? Where does it make mistakes? Who needs to check the result? Those answers decide whether the idea deserves a larger investment.
Three days forces a narrow scope: one workflow, a handful of real examples and one visible result. The constraint is the point. It turns an open-ended AI project into a decision you can make in a week.
What you get
Something real, and a clear verdict.
The prototype matters, but the findings matter more. They tell you what to do next.
A walkthrough with your team
We run it together, including the cases where it gets things wrong, so people see its real behavior.
Clear findings
What worked, what did not, and what data, process or review the use case would need in production.
A clear recommendation
Proceed to a production build, adjust the idea, or stop. With the reasoning written down.
An outline of the path to production
What would change next: access controls, evaluation, integration and operations. That is the work Enterprise AI Hardening covers.
How it works
One clear step at a time.
Choose and prepare
Before day one, agree the use case, what success looks like, the sample inputs and who will review the results.
Day one: frame and build
Set up the pipeline and get the first complete flow running from input to visible output.
Day two: test on real examples
Run your samples through it, review the failures with your expert and improve what can be improved in the time.
Day three: show and decide
Walk through the prototype with your team, then record the findings and the recommendation.
What changes
From where you are to where you want to be.
From: A promising demo you cannot trust
To: A prototype tested on your own examples
From: A decision based on a pitch
To: A decision based on something you watched happen
From: An open-ended AI project
To: A bounded experiment with a clear stop or go
Related thinking
What a good prototype should teach you.
Before you start
What to expect.
A prototype is not a product.
It will not have production security, scale or integration. That is the work that follows, and we will say what it involves.
Some ideas will not fit in three days.
If a use case needs more time to test fairly, we say so before we start.
Questions
Common questions.
What kinds of use cases suit three days?
Ones where the input and the output are clear: reading documents and extracting or summarizing them, answering questions from a body of knowledge, preparing a quote or a reply from structured inputs, or sorting incoming requests. Work that depends on many deep system integrations suits a different shape, and we will say so.
What do you need from us?
A chosen use case, a set of realistic sample inputs, and a person who knows what a good result looks like and can review ours during the three days.
What if our data is sensitive?
We agree data handling, model-provider terms and any NDA before we touch your data. Redacted or synthetic samples are often enough for a proof of concept.
Who owns the prototype?
You own what we build for you, including the code, unless we agree otherwise in writing.
What if we do not know which use case to pick?
Start with an AI Innovation Workshop. It is designed to produce exactly that choice.
What happens after the three days?
You decide. Some teams proceed to a production build, some adjust the idea and some stop. All three are good outcomes of a well-run experiment.
Pick the use case. We will bring the three days.
Tell us what you want to test. We will help you choose the scope that gives you a fair answer.