AI Proof of Concept Development
An AI proof of concept is a structured, time-boxed test of a specific AI idea against real business data and criteria, producing a clear go/no-go recommendation and rough cost estimate for a full build.
Benefits
- A clear, evidence-based go/no-go decision
- Rough cost and timeline estimate for the full build
- Lower risk than committing to a full project up front
Problems It Solves
- Needing evidence before committing budget to a full AI project
- Board or leadership requiring proof before sign-off
- Uncertainty about likely accuracy or ROI at production scale
Who It's For
- Businesses evaluating a significant AI investment
- Leadership teams needing sign-off evidence
- Enterprise and mid-size businesses
Common Use Cases
- Validating AI accuracy against a real data sample before a full build
- Estimating ROI for an automation project
- Comparing two technical approaches before choosing one
How We Deliver It
- 1
Success criteria
We agree measurable criteria for what a successful outcome looks like.
- 2
Build and test
We build against real data and measure against the criteria.
- 3
Recommendation
You get a written recommendation with cost and timeline for full build.
Technologies
- OpenAI API
- Anthropic Claude
- Evaluation frameworks
FAQs
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