AI proof of concept
Doubts about using AI? We put the first building blocks in place and reassure you that it works.
First building blocks · on your case · live demos
- Format
- First building blocks set up on your own case
- Who is involved
- Your IT team and the users of the chosen use case
- What you leave with
- A working prototype on your case and a clear answer to your doubt
- What comes next
- Production, or data preparation if the data is what blocks you
Who needs an AI proof of concept
A proof of concept fits when an AI idea looks promising but no reliable answer exists yet to the question that matters: will it work here, with our documents and our people?
- A use case looks promising, but you doubt it is feasible before committing a budget.
- Your leadership wants to see a concrete result before funding a broader AI project.
- You want to check that a model performs well on your own data, not on a demo example.
- Two approaches compete for the same question, and only a side-by-side trial can settle it.
From doubt to decision, in four steps
- The doubt to resolve: we choose the use case and what the proof has to show.
- The first building blocks: we build the minimal version: model, data and interface.
- The trial: your users try the prototype, and we note what works and what does not.
- The decision: continue, adjust or stop, based on what we saw.
To see what a proof of concept looks like, browse our demonstrators.
See the demonstrators →How an AI proof of concept is run
A proof of concept starts with a precise question, worded before any code is written: what must we see working to decide to go on? That question fixes the use case, the data used and what counts as success. We then build the minimal version, with only the model, data and interface needed to answer it, because every element added delays the answer without making it more reliable.
The trial runs with your users, on the case you chose, because only they know whether the tool's answer is useful in their work. We note what works and what does not, including when the blockage comes from the data rather than the model. The outcome is a decision: continue, adjust or stop. A documented stop has value, since it keeps you from investing in a direction the trial has ruled out.
What you get
A prototype on your case
A minimal version running on your use case, with the model, data and interface it needs, that your users can try for themselves.
A finding on what works
A summary, drawn up with your users, of what meets the need and what does not, whether the cause is the model, the data or the interface.
A decision based on the trial
A recommendation to continue, adjust or stop, resting on what the trial showed on your own case rather than on a vendor's promise.
After the AI proof of concept
The decision that closes the trial points to what comes next. If you continue, production takes the prototype over and turns it into a service your teams can use every day. If the trial mostly exposed scattered sources or unclear access rights, data preparation comes first, and a new trial starts again on a cleaner base.
Frequently asked questions
What is an AI proof of concept?
It is a minimal version of an AI project, built on a specific case to check that the idea works before you invest further. It answers a question set at the start, for example: does the tool find the right information in our documents?
Can a proof of concept fail?
Yes, and the answer is still useful. It more often stumbles on the data than on the model: scattered sources, outdated content, unclear access rights. In that case, the next step is to prepare the data.
What happens to the proof of concept if it succeeds?
It becomes the basis for production. A prototype does not have the safeguards of a service used every day; going to production adds monitoring of answer quality, costs and incidents.
Is a proof of concept run on our real data?
It is run on your case, so with the data for that case, as far as the question to resolve requires it. The choice of data and hosting is made at the start, with Québec as the default, so that the trial respects your confidentiality rules.