From PoC to MVP: How to Turn a Proof‑of‑Concept into a Real Product

By Amin Rabinia · Founder, Glissando AI

Learn how to move beyond paper prototypes and PoCs to build a fully functioning Minimum Viable Product (MVP) — with practical steps and mindset shifts.

1. Write It All Down

Start by documenting your idea clearly. A Proof of Concept (PoC) demonstrates that your concept is valid and makes sense to explore further.

2. Understand the Difference Between PoC and MVP

A PoC is a concept-level demo—intended to validate feasibility, without a real-world footprint. An MVP is an actual product, still early-stage, but complete enough to test with real users.

3. Steps to Move from PoC to MVP

1. Survey the Landscape: Leverage existing solutions to accelerate building your MVP, then add your unique twist.
2. Build & Iterate: Assemble components, test, refine, add features gradually.
3. Stay Lean: Focus only on what’s essential—don’t over-engineer or over-plan.
4. Keep Your PoC Visible: Use it as reference—your roadmap back to the core idea.
5. Launch for Feedback: Release your MVP to real users. Collect input and iterate in a feedback loop.

Watch how this strategy works in action...

4. Why Generative AI PoCs Stall Before Production

The generic PoC-to-MVP advice above applies to any software. Generative AI adds a specific failure mode on top of it, and it catches teams by surprise: the PoC works because a human is standing next to it.

In a demo, someone picks a good input, reruns it when the output is wrong, and narrates around the rough edges. In production, nobody does any of that. The same system now has to handle inputs nobody anticipated, fail safely when it's wrong, and produce something a customer sees without a human in the loop smoothing it over. That gap is where most GenAI PoCs stall.

Four things reliably have to be solved between a working GenAI demo and something you can put in front of customers:

• Latency the architecture can't absorb. If your generation step takes minutes, no amount of model optimization fixes it — the request shape is wrong. It has to become a fast preview plus a background job. We hit exactly this building a video product where a full render takes 10–20 minutes: why AI generation apps need a background job queue.
• No way to know if it's working. A demo is judged by "that looks right." Production needs an actual definition of correct, per use case. Most teams check the one or two risks they already imagined and miss the rest — the fix is a structured evaluation matrix, not a longer checklist.
• Secrets and access that were fine in a prototype. An API key that lived in a config file during the demo becomes a real liability the moment the thing is public. That is not a hypothetical for us — we had a vibe-coded early build expose a key, and the remediation was never just "rotate it," it was auditing everywhere a secret could sit in plaintext.
• One giant prompt doing five jobs. It holds together in a demo and becomes undebuggable in production, because when the output is wrong there's no single place to look. Splitting by real job boundaries is what makes a system fixable later: how many AI agents your product actually needs.

None of these are exotic. They're just invisible while a human is driving the demo — which is why the PoC feels finished and the MVP takes three times longer than anyone budgeted.

5. Expect Version One to Be Weak — On Purpose

The most useful mindset shift is accepting that your first production version will underperform the demo, and planning for it out loud rather than being surprised by it.

On one procurement build, version one had low accuracy, low speed, and low coverage — and that was communicated to the client up front. The goal wasn't a finished system; it was getting the pipeline doing the most basic version of the task end to end, then improving accuracy against real production data. Recommendation accuracy passed 90% after the second build iteration, not the first. That only worked because expectations were set against a roadmap instead of against the demo.

A PoC that gets treated as a nearly-finished product is the single most expensive misunderstanding in AI development. Treat it as permission to start, not proof you're almost done.

Inspired by Amin Rabinia’s article. Read the full piece on Medium.

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