How long does a generative AI proof-of-concept take?

Most focused proof-of-concepts run 4-8 weeks, assuming the data needed is already accessible. Timelines stretch when data access, security review, or stakeholder sign-off take longer than the build itself.

Is a proof-of-concept the same as a pilot?

No. A proof-of-concept answers whether an approach is technically feasible, often with a narrow, controlled dataset. A pilot runs the solution against real users or real workflows to validate business value before a full rollout.

What's the biggest hidden cost in a generative AI project?

Data readiness. Cleaning, structuring, and securing access to the source data a model needs almost always takes longer and costs more than the model integration work itself.

What Does a Generative AI Proof-of-Concept Cost in 2026?

Published August 16, 2026

"How much does a generative AI project cost?" is usually the wrong first question — the honest answer is "it depends," and the range is wide enough (a few thousand dollars to well into six figures) that the number alone isn't useful. A more useful question is what actually drives that cost, so you can estimate your own project instead of anchoring on someone else's number.

Model choice matters less than people think

Calling a hosted LLM API is usually a small line item relative to the rest of the build — often a few hundred to a few thousand dollars a month depending on volume. Fine-tuning a model or self-hosting one changes that math significantly, but for most proof-of-concepts, API costs are not the budget driver.

Data readiness is the real cost driver

The work that actually consumes budget: getting your data into a state the model can use. That means identifying the right source systems, extracting and cleaning documents, setting up secure access, and — for RAG-based projects — chunking and embedding content into a retrievable format. If your documents live in a well-organized system already, this is fast. If they're scattered across shared drives, email, and legacy systems with no consistent structure, this step alone can take longer than building the model integration.

Integration and evaluation

Two more line items that get underestimated:

Integration — connecting the AI component to whatever it needs to plug into: your CRM, your document store, your internal tools, your authentication system. This is standard software engineering work, and it scales with how many systems are involved.
Evaluation — testing the model against real examples to measure accuracy, catch hallucinations, and confirm it behaves correctly on edge cases before anyone relies on it. Skipping this step is how "it worked in the demo" projects fail in production.

A rough shape for a focused PoC

For a single well-scoped use case — one document type, one workflow, a defined success metric — a 4-8 week proof-of-concept is a realistic timeline once data access is sorted out. Broader scope (multiple data sources, multiple user roles, compliance review) extends both timeline and cost. We've built this kind of narrow, fast PoC across use cases like document Q&A and custom generative AI models, and the pattern holds: the model is rarely the bottleneck — your data and your integration surface area are.

Getting a real number

The only way to get an accurate estimate is to scope your specific use case: what data it needs, what systems it touches, and what "working" means for your team. Get in touch and we'll help you size the project before you commit budget.