Define the outcome
User, process, input data, quality criteria and the cost of error.
AI prototype · MVP · hypothesis validation
Test AI's value
before a big rollout
I build a constrained working prototype on a real process and data set — so you can measure quality, economics and risk, then decide whether to scale the system.
When it helps
You have a task but no proof that a complex system will pay off
You need to test an agent, knowledge retrieval, analytics, content generation or a workflow integration.
You're unsure whether you have enough data and what quality is achievable in practice.
You need to compare local and cloud models on quality, speed and cost.
You want a working scenario with clear constraints, not a presentation.
MVP scope
Hypothesis → prototype → measurement → decision
Assemble the minimum viable system
Model, tools, knowledge base and integrations — only what's needed.
Test on examples
Typical and edge scenarios, speed, cost and human involvement.
Prepare a decision package
Demo, test results, limitations and a plan for the next stage.
Deliverables
Materials you can decide on
Working prototype
Constrained but reproducible user scenario.
Quality assessment
Criteria, test examples, errors and applicability conditions.
Economics
Operational cost estimate, effort and expected return.
Next-stage architecture
Data, models, tools, integration bus, security and control.
Boundaries
An MVP doesn't masquerade as a production system
Success criteria and experiment limits are defined up front. If the hypothesis isn't confirmed, the result is an evidence-based no-go or a narrower scenario — without artificially inflating the project scope.
First step
Describe the process and the expected outcome
It's enough to share the task, sample input data, current way of working and desired effect.
Write about your AI hypothesis →