Rapid prototypes (PoC)
A rapid prototype (PoC, proof of concept) is a scoped, time-boxed test that checks, on your real data, whether an AI approach works for a specific problem. It ends with a decision: continue, adjust or stop.
Updated October 11, 2026
The reality
AI proofs of concept make it to production, according to IDC and Lenovo. IDC attributes this to a lack of readiness: data, processes, infrastructure.
Source: CIO.com, March 25, 2025
of proofs of concept are abandoned before production by the average organization, according to S&P Global Market Intelligence (survey of more than 1,000 respondents).
Source: CIO Dive, March 14, 2025
of generative AI projects will be abandoned after proof of concept by the end of 2025, Gartner predicted: data, risks, costs, unclear value.
Source: Gartner, via CRN India
These three figures don't measure the same thing: don't compare them with each other. What they share: failure comes mostly from the organization (unclear goal, data not ready, no decision), not from the technology.
The stages of an AI project
Each stage answers a different question. Rapid prototypes cover the first two: they let you decide before moving to a pilot.
Boundaries vary by author. Sources: Multimodal · Kanerika.
Before writing a line of code
If criteria are defined after the results, there is no objective basis left for deciding. We write them first, with you.
Sources: SoftwareSeni · Grid Dynamics · HSO.
A typical timeline
Here is an example eight-week timeline. Simple cases move faster; the actual schedule is set with you during scoping.
Indicative example based on provider guides (one week of preparation, then build, evaluation and decision), not a delivery commitment. Sources: Team400 · HSO.
The decision
A prototype that can't change your decision is just a lab project. Ours always ends with a report against the starting baseline and a clear recommendation.
The threshold is met: we prepare a pilot with an estimated scope and production effort.
The result is close: we fix one specific point (data, prompt, scope) and measure again.
The threshold can't be reached: you save the investment. That's a useful result, not a failure.
What you receive
Avoid the trap
Causes cited by IDC (data, ROI, skills) and S&P Global (cost, privacy, security). See also our pages on ERP and CRM integration and support and monitoring.
In Tunisia
For 70.9% of Tunisian companies, the barrier to digital innovation is financial, and for 63.3% it is the skills shortage (ITCEQ, May 2026). A bounded prototype that can be stopped limits risk before any larger investment.
Source: WMC/TAP citing ITCEQ, June 3, 2026.
Our approach
One question, one business owner, one metric and its threshold.
Your real data and the starting baseline.
The simplest demonstrator that answers the question.
On held-out data, including cost and security.
Continue, adjust or stop, with the production effort estimated.
FAQ
A scoped, time-boxed test that checks, on your real data, whether an AI approach works for a specific problem. It ends with a decision: continue, adjust or stop.
A PoC checks feasibility, a prototype shows how users work with the solution, and a pilot tests it in real conditions with real users. Boundaries vary by author.
Typically 4 to 8 weeks, with one week of preparation before kickoff. Simple cases can move faster. The actual schedule is set with you during scoping.
According to IDC, because of a lack of readiness: unclear goal, data not ready, lack of in-house expertise. S&P Global adds cost, privacy and security. Setting criteria before building reduces this risk.
You've avoided a larger investment in a solution that didn't hold up. The report explains why and indicates whether to adjust the scope or data, or to stop.
The prototype is built in a secure environment with limited access rights. We agree with you on what is used and retained, especially for personal data.
Go further
Tell us about a use case: together we'll define the question to answer, the success criterion and the timeline.