AI and Gen AI proof of concept: proof, not hype
Validate the right use case, prove business value and de-risk your AI investment on Google Cloud, before you fund a full build.
What is an AI proof of concept?
An AI proof of concept is a small, focused build that tests whether an AI idea works on your data and is worth funding. A good one ends with a working demo, measured evidence of value and a plan for production, not just a slide.
What ships at the end
A working demo
Stakeholders can try key flows, guardrails and success metrics tied to your goals.
Evidence of value
Time saved, accuracy and quality scores, plus a simple cost model for go/no-go.
A production blueprint
Architecture, data flows, compliance notes and a phased plan to an MVP.
Vertex AI and Gemini
Models, prompt orchestration and evaluation, with BigQuery or AlloyDB for data.
Search and RAG
Vector search with Vertex AI or BigQuery, served on Cloud Run or GKE.
CI/CD and observability
Cloud Build, Workflows and Logging from the first version.
Common questions
Who is an AI POC for?
Startups and SMBs validating before a full build, product and ops teams with repetitive work, and data and IT leaders who need security and governance from day one.
What do we get at the end?
A working demo, evidence of value with a cost model, and a production-ready blueprint.
What if the POC shows it isn't worth it?
That's a good result too: you've avoided funding the wrong build.
How do we start?
Book a free POC scope call to pick the use case and success metrics.