The gap between an AI demo and an AI deployment is where most projects fail. A demo uses curated data, handles the happy path, and impresses a room. A deployment handles edge cases, bad data, user error, and the workflows nobody mentioned in the requirements meeting.
The Prototype & Validation engagement bridges that gap. We take your highest-priority AI opportunity and build a functional prototype that runs against your real data, integrates with your actual workflows, and gets in front of your actual users.
We're not building a slide deck with mockups. We're building working software. If the opportunity is document classification, the prototype classifies your documents. If it's intelligent search, the prototype searches your data. If it's automated estimation, the prototype produces estimates your team can compare against their manual process.
The build phase takes one to three weeks depending on complexity. We work with your existing data sources and integrate with your current tools where it makes sense. The goal is a prototype that's real enough to measure and rough enough to build fast.
Then we validate. We put the prototype in front of the people who would use it daily. We measure: does it produce accurate results? Does it save time? Do users trust it? What breaks? What's missing? We collect structured feedback and quantitative data.
The validation report gives you three things. First, measured results: accuracy rates, time savings, error rates, user satisfaction. Second, a production roadmap: the architecture, infrastructure, and engineering work required to take this from prototype to production, with a cost model covering build, deployment, and ongoing operation. Third, a build-or-buy recommendation: should you build this capability internally, buy a commercial solution, or integrate an API?
This engagement is designed to de-risk the decision. If the prototype validates the business case, you have the data to justify the investment. If it doesn't, you spent two to four weeks instead of six months finding that out.
What you get
- 01Working prototype tested against your actual data and workflows
- 02Validation report with measured results and user feedback
- 03Production roadmap: architecture, cost model, timeline, and build-or-buy recommendation
How it works
Scope & Data Integration
Define the prototype scope. Connect to your real data sources and existing systems.
Prototype Build
Build working software that handles your actual use case with your actual data.
User Validation
Put the prototype in front of real users. Measure accuracy, time savings, and trust.
Results Analysis
Compile quantitative results and qualitative feedback into a validation report.
Production Roadmap
Deliver architecture, cost model, timeline, and build-or-buy recommendation.
Best for
Companies with a specific AI use case in mind that need to validate it before committing budget, or companies coming out of an Opportunity Sprint ready to test the top recommendation.
Have an AI use case you need to validate?
Book a call to discuss your opportunity and whether a prototype engagement fits.
Let's Talk