The MVP should answer a question, not impress in a demo
A minimum viable product with AI is used to check whether a proposal solves a problem and whether it can do so with acceptable quality, cost and risk. It is not a small version of the final product or an excuse to integrate every available feature.
Start with a hypothesis arising from problem validation. Define which decision you will make with the MVP result and what minimum evidence is needed to move forward.
An MVP’s function is not to look finished. It is to prevent you from spending months building something you have not yet proved should exist.
Choose the prototype according to the uncertainty
- Clickable prototype: validates navigation, comprehension and expectations.
- Concierge: the team manually provides the service to validate value and process.
- Wizard of Oz: the interface appears automated, but part of the operation is human.
- Rules and branching logic: test the flow without depending on a model yet.
- AI API: validate quality, latency and cost with real capability from the start.
Be transparent when a person believes they are interacting with AI. Simulation is a learning technique, not permission to deceive or hide human intervention.
When to use no-code
No-code platforms accelerate interfaces, databases and automations. They are suitable when the goal is learning, volume is limited and integrations are standard. They also allow business experts to participate directly in the prototype.
Assess their limits: permissions, export, performance, observability, compliance, cost at scale and vendor dependence. Document from the start which components could be migrated if the use case works.
Design the minimum architecture
- Input with only the strictly necessary fields.
- Versioned instructions and context.
- Interchangeable model or API whenever possible.
- Output validation and security rules.
- Human review for exceptions.
- Record of use, result, cost and feedback.
Do not use real personal data if you can validate with synthetic or anonymised information. If you need real data, define purpose, access, retention and provider before uploading it. Relate these decisions to the GDPR applied to AI.
Measure four types of evidence
- Value: the user completes the task better or is willing to continue or pay.
- Quality: correct, useful and consistent results by case type.
- Experience: time, abandonment, understanding, trust and frustration.
- Viability: unit cost, latency, data, integration and review workload.
Compare it with the current process. Measure acceptance and correction rates, not just satisfaction. Record failed cases: they often teach more about limits and requirements than the examples selected for the demonstration.
Avoid these mistakes
- Building before talking to users.
- Choosing a tool before defining the hypothesis.
- Using only easy cases in the tests.
- Ignoring hidden manual work.
- Measuring accuracy without measuring usefulness.
- Moving the prototype into production without redesigning security and operations.
Criteria for the next phase
Move forward if there is a value signal, the system reaches a defined quality threshold and the cost can be sustained. Correct course if the friction lies in the flow or data. Stop if the need is not urgent or a simpler solution achieves the same result.
MVP experiment sheet
- Main hypothesis and risk.
- User and test scenario.
- Version and exact scope.
- Metric, baseline and threshold.
- Permitted data and controls.
- Manual work behind the prototype.
- Result, learning and decision.
Test normal, ambiguous and adversarial cases. Include incomplete inputs, contradictory instructions and situations where the system must refuse. An MVP that only works with prepared examples validates a demonstration, not an operation.
Frequently asked questions about AI MVPs
Should I use the most powerful model?
Not necessarily. Use the model that reaches the threshold with suitable cost, latency and conditions. Design evaluations that allow you to replace it later.
What if no-code works very well?
It can stay in place if it meets performance, security, governance and scale-economics requirements. Review dependence, export and continuity before turning it into critical infrastructure.
When is custom development worthwhile?
When differentiation, integration, control, scale or cost cannot be solved sustainably with standard components. The decision should be based on MVP evidence.
The MVP that gave rise to I3OS
At Impulsa3 we used the SEO pilot as an automation MVP: a focused way to check whether we could combine context, data, tools, method and human review. When that pattern worked in real tasks, we applied it to more clients and areas until it became I3OS. We did not try to build the complete system from day one; we validated one capability, measured the learning and expanded the scope.
If you need to turn an opportunity into a measurable and secure AI MVP, Impulsa3 can help you design, build and validate the solution before scaling it.
