Rapid Prototyping Lab
A two-week lab that turns a risky idea into a user-tested prototype and a technical spike, ending in a clear build, park, or pivot call.
- Timeline
- 2 weeks
- Team
- architect, UX, FE, MLE/BE (spike)
- Typical stack
- Figma or code-based prototype; tech spike in target stack (Python/Node/Rails); cloud or on-prem
What you get
- 2-week lab
- user-tested prototype
- tech spike
Outcomes
- Clickable prototype covering 5-7 critical flows
- Tech spike code for riskiest section (e.g., RAG, CV, SSO)
- Risk register with cost/effort T-shirt sizing
- Clear decision: proceed, park, or pivot
Selected work
Where teams use it
Manufacturing
CV-based defect detection feasibility
Prototyped defect dashboard + spike on model accuracy for top 3 defect types. Validated 91% accuracy, greenlit with $1.2M/yr savings projection.
Retail
GenAI personalization validation
Clickable e-commerce flow + RAG spike with product catalog. User testing revealed checkout friction; pivoted design, de-risked integration.
Finance
Fraud detection ML + compliance
Tech spike on model precision/recall + compliance framework prototype. Proved 94% precision, mapped audit trail requirements, greenlit for build.
What we need from you
- User journeys or workflow maps
- Riskiest technical assumption (RAG accuracy, latency, integration)
- Target users for testing (5+ participants)
- Decision criteria and stakeholders
- Budget and timeline constraints
Proof points
- User testing session recordings + synthesis
- Tech spike benchmarks (latency, accuracy, feasibility)
- Risk register with mitigation options
- Decision brief with next-step recommendations
Frequently asked questions
A working functional prototype that demonstrates the core AI capability against your real data. Not wireframes, not slide decks. You get a testable application with measured accuracy, latency benchmarks, and a clear go/no-go recommendation. The prototype includes enough infrastructure to run user tests with actual stakeholders.
Because the demo was built to impress, not to survive contact with real data. The common failure pattern: cherry-picked examples, no evaluation baseline, no owner after the demo, and an architecture that has to be thrown away to scale. We build prototypes the opposite way: your data from day one, an evaluation harness with agreed thresholds, and production-shaped architecture, so the decision at the end is scale it or stop, not start over.
Sometimes it isn't, and the prototype exists to find that out cheaply. If the rules can be fully written down, deterministic automation beats a model: cheaper, faster, auditable. AI earns its complexity when inputs are messy (documents, images, conversations) or the rules cannot be specified completely. When the prototype shows a rules engine would do, we say so; a 2 to 3 week finding that saves you a six-month build is a good outcome.
Three possible outcomes. If the prototype validates the concept, we scope a production buildout with realistic timelines. If it partially validates, we adjust the approach and run another focused iteration. If it fails to meet your criteria, you have a clear technical analysis explaining why, which saves you months of building the wrong thing.
Ideally yes, but we can work with limited data. If you have existing datasets, we use those. If not, we can generate synthetic data for initial validation or use publicly available datasets that approximate your domain. The prototype's accuracy improves significantly with real data, which is why we recommend providing it if possible.
Fixed-scope engagement with a defined deliverable and timeline. You know the cost before we start. No hourly billing surprises, no open-ended discovery phases. The prototype deliverable and success criteria are agreed upon in the first two days.