How do I run an AI proof of concept before investing?

Pick one workflow with a measurable outcome, insist the PoC runs on your real data rather than samples, and write the success criteria down before work starts. Timebox it hard — two weeks is enough for a scoped question — and require a written go/no-go recommendation with a cost-to-production estimate. If a vendor resists any of those conditions, that is your answer about the vendor.

How much does an AI proof of concept cost?

As of 2026, U.S. market pricing for an AI proof of concept typically runs from around $10,000 to $50,000 depending on scope and data complexity. Blankpage prices the two-week PoC as a fixed fee quoted before work begins — no change orders, no pricing theater. The number exists so a bad idea costs two weeks, not two quarters.

How long does it take to build a custom AI solution?

Two weeks to a validated proof of concept on your data, then six to twelve weeks from there to a production deployment, depending on integrations and compliance requirements. That is the honest range for most custom AI systems. Vendors quoting a year of discovery before anything runs are selling process, not software.

What happens if the proof of concept fails?

You keep everything: the prototype, the architecture memo, the cost estimate, and a written explanation of why the use case does not clear the bar. A no-go after two weeks is a result — it typically saves months of committed spend on a system that should not exist. We would rather lose a build engagement than deliver one we cannot defend.

AI Proof of Concept in Two Weeks

A working AI proof of concept in two weeks. Not a slide deck in six. Fixed scope, fixed price, your data — and a decision you can defend to the board.

What AI proof of concept development actually is

AI proof of concept development is a short, fixed-scope engagement that tests whether a specific AI use case works on an organization's real data before serious money is committed. A credible AI proof of concept produces three things: a working prototype, an architecture plan for production, and an honest cost estimate — not a demo on synthetic data attached to a proposal for more consulting.

Ours takes two weeks. That is the whole pitch, and it is enough — if the scope is locked on day one and the team reads your data instead of your org chart.

The problem: pilot purgatory

The industry pattern is familiar. A discovery phase that lasts a quarter. A demo built on curated samples that no production system will ever see. A readout deck that recommends a bigger engagement. Competitors call four weeks fast; most enterprise AI pilots never reach production at all, and everyone in the room knows it.

The fix is not more process. It is a smaller question, answered honestly: does this specific use case work on this specific data, and what would it really cost to run? That is a two-week question.

How the two weeks run

Days 1-2: scope lock. One use case, your data connected, success criteria in writing and agreed by both sides. Days 3-8: build. A working prototype against your real data — documents, tickets, transactions, whatever the workflow touches — with daily progress you can see, not weekly status calls. Days 9-10: evaluation and the memo. We measure the prototype against the agreed criteria, price the path to production, and put a recommendation on paper.

Day 10 ends in a working session: live demo, real numbers, and a go/no-go you can take to the board without translating consultant-speak.

Fixed deliverables. All of them yours.

Every AI proof of concept ships the same four artifacts: (1) a working prototype running on your data, with source code; (2) an architecture memo describing the production system — components, integrations, security posture; (3) a cost-to-production estimate covering build cost, run cost, and timeline; (4) a written go/no-go recommendation with the reasoning shown. Full IP transfer. You can take the memo and build with anyone, including without us.

And yes: some PoCs end in no-go. That is not a failed engagement. That is the engagement working — two weeks of certainty instead of two quarters of sunk cost.

What happens after two weeks

If the answer is go, most clients move to a 6-12 week Design & Build — whether the system is an AI agent, a private LLM on your infrastructure, or workflow automation from our broader AI systems practice. If you want counsel rather than construction, the Strategic Advisor seat is ongoing. The terms of all three engagement models are public: fixed scope, clean exit, no lock-in.

Frequently asked questions

How do I run an AI proof of concept before investing?: Pick one workflow with a measurable outcome, insist the PoC runs on your real data rather than samples, and write the success criteria down before work starts. Timebox it hard — two weeks is enough for a scoped question — and require a written go/no-go recommendation with a cost-to-production estimate. If a vendor resists any of those conditions, that is your answer about the vendor.
How much does an AI proof of concept cost?: As of 2026, U.S. market pricing for an AI proof of concept typically runs from around $10,000 to $50,000 depending on scope and data complexity. Blankpage prices the two-week PoC as a fixed fee quoted before work begins — no change orders, no pricing theater. The number exists so a bad idea costs two weeks, not two quarters.
How long does it take to build a custom AI solution?: Two weeks to a validated proof of concept on your data, then six to twelve weeks from there to a production deployment, depending on integrations and compliance requirements. That is the honest range for most custom AI systems. Vendors quoting a year of discovery before anything runs are selling process, not software.
What happens if the proof of concept fails?: You keep everything: the prototype, the architecture memo, the cost estimate, and a written explanation of why the use case does not clear the bar. A no-go after two weeks is a result — it typically saves months of committed spend on a system that should not exist. We would rather lose a build engagement than deliver one we cannot defend.