AI MVP · VALIDATION PLAN
AI MVP validation plan: what to prove before production
An AI MVP is not a demonstration of every future feature. It should retire the most expensive uncertainties by separating user value, data fitness, AI quality, end-to-end workflow behaviour, and operational readiness.
01 · DIRECT ANSWER
Direct answer
Before production development, an AI MVP must answer four independent questions: does the user need the workflow, are the data fit for purpose, is quality acceptable given error severity, and does the full path work in the real process? If any answer remains an assumption, the next stage should test that risk rather than scale the product.
02 · CRITERIA
Criteria before development
Apply these criteria to the specific workflow, data, and cost of error. They are not a universal readiness checklist.
Falsifiable hypothesis
The user, action, and observable signal that would support or reject value are stated. A feature list is not a hypothesis.
Representative examples
The evaluation set reflects real workflows, rare difficult cases, and unacceptable errors; provenance and data rights are known.
One vertical path
A user can complete the core workflow from input to outcome. Manual operations are acceptable when explicitly labelled and included in the decision.
Comparable alternative
The current process or a simpler option is recorded. Without it, the team cannot tell whether the MVP improves the task or merely looks novel.
Continuation rule
Before the pilot, define which observation will lead to further investment, a changed hypothesis, or a stop.
03 · PROCESS
Validation sequence
- 01
Separate the risks
Map uncertainty across user value, data, model quality, UX, integrations, security, and economics.
- 02
Test the critical component
If model quality is unknown, start with an evaluation set; if the workflow is unknown, start with a prototype or manual simulation.
- 03
Build a thin vertical slice
Connect interface, data, AI, and outcome in the smallest scope while logging errors and human intervention.
- 04
Run a bounded pilot
Choose a defined audience and observation period, and avoid silently changing the workflow version during the test.
- 05
Decide from evidence
Compare results with the original hypothesis and decide separately what to scale, redesign, or stop.
04 · DECISION
Decision table
Choose the validation format from the largest uncertainty, not from the desired completeness of the product.
| What is unknown | Minimum test | What it reveals |
|---|---|---|
| Whether users need the workflow | Interviews, prototype, or manual service | Reveals behaviour and value before a complex system is built. |
| Whether usable data and rights exist | Data audit and sample | Separates available material from assumptions about a future dataset. |
| Whether AI can perform the task | Offline evaluation on a held-out set | Supports comparison and analysis by error type. |
| Whether the end-to-end process works | Logged vertical MVP | Exposes UX, integration, and operational failures. |
| Whether production investment is justified | Bounded pilot against the baseline | Connects product quality with an actual work outcome. |
05 · MEASUREMENT
What to measure
Record the baseline workflow and collection method first. Then compare equivalent scenarios without presenting a planned target as measured impact.
Workflow value
Target-task completion, repeat use, abandonment, and qualitative reasons for behaviour within the selected user group.
Quality and error severity
Evaluation-set results by category, including critical failures, abstentions, and cases requiring human intervention.
End-to-end operation
Latency, technical failures, integration success, and workflows that cannot complete without hidden manual work by the team.
Baseline comparison
Change in time, manual steps, quality, or throughput relative to the process recorded before the pilot.
Operating cost
Model, infrastructure, integrations, human review, support, and error correction are counted together, without claiming ROI before a real comparison.
06 · BOUNDARIES
Risks
The polished-demo trap
Curated examples hide the distribution of real requests and do not expose abstentions, latency, or manual corrections.
Evaluation leakage
If control examples shape tuning and are then reused as an independent test, the result becomes optimistic.
Unrepresentative pilot
A convenient early-user group or cleaned dataset may not reflect future load and workflow diversity.
Hidden manual operations
Manual data preparation and answer correction can aid learning, but must remain visible in cost and scaling plans.
WHAT CANNOT BE CLAIMED
Limitations of the conclusion
- An MVP validates a bounded workflow; it does not prove that the architecture is ready for all future load.
- A small pilot does not provide a universal forecast of business impact; results depend on the audience and process.
- External-model quality can change after an update, so versioning and repeat evaluation are mandatory.
- For high-impact decisions, an MVP does not remove security, access control, legal review, or human oversight.
07 · PORTFOLIO
Related projects and honest stage
The related projects are at technical or local MVP stage. These labels do not mean production operation or validated commercial impact.
Marketing Optimisation
The technical MVP implements one end-to-end CVM reactivation workflow and has been tested on synthetic data. The next step is a design-partner pilot with real CRM data; a production replacement for the department and commercial impact have not yet been validated.
Open project reviewGadalnya
The local MVP implements the core flow, server API, validation, and a demo response in local demo mode. A public launch, real payments, and production LLM configuration have not yet been completed or verified.
Open project review08 · METHODOLOGY
Primary sources
These sources support the methodology and definitions. They do not validate AI SaaS Solution project outcomes.
The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
Open sourceHidden Technical Debt in Machine Learning Systems
Open sourceArtificial Intelligence Risk Management Framework (AI RMF 1.0)
Open source