AI PoC Timeline Estimator: How Long Until You Have a Working Demo
This free AI PoC timeline estimator predicts how many weeks a proof of concept will actually take, and it is built for product owners and IT leads who need a realistic date to give stakeholders rather than an optimistic one. Enter use case complexity, data access lead time, integration depth, and how many evaluation cycles you expect, and the tool returns total weeks and months to a stakeholder-approved demo. The most common cause of a blown PoC timeline is not the AI build itself, it is data access approval, which routinely takes longer than the entire technical build combined.
Your numbers
Base build time for the core AI logic before integration and evaluation are added.
Time for legal, security, and IT approval before the team can actually touch representative data.
Additional weeks to connect the PoC to a real system rather than run as a standalone demo.
Rounds of testing against real questions and revising the approach based on results.
Time to run one round of evaluation and act on the findings.
Time for business stakeholders to review the demo and sign off before the PoC is considered complete.
Your results
Planning estimate only. Data access delays in particular are notoriously hard to predict; validate your legal and security approval timeline before committing to a date with stakeholders.
Get your full PoC timeline and risk plan
We will email you a personalized week-by-week PoC schedule with data access and evaluation planning, and a Netray consultant will follow up to review it against your review process.
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Where PoC timelines actually go wrong
With the defaults, a moderate RAG use case takes 4 build weeks plus 1 week of light integration for a 5-week build phase, 3 evaluation cycles at a week each for 3 weeks, and 2 weeks combined for data access and stakeholder review, totaling 11 weeks, or roughly 2.5 months. In practice, data access is the input teams most consistently underestimate: legal review of a data processing agreement, security's assessment of a new AI vendor, and IT provisioning access to a representative dataset frequently take longer than the entire build phase, and none of that time is visible on an engineering sprint board.
- Start the data access request in week one, in parallel with technical setup, never after the build begins.
- A demo on synthetic or sample data is not the same PoC as one on real production data; budget separately if you need both.
- Three evaluation cycles is a reasonable floor; fewer than that usually means the team is demoing rather than actually testing.
- Stakeholder review time compounds when more than one business owner needs to sign off separately rather than in one session.
What a good PoC actually needs to prove
A PoC exists to answer one question: does this approach work well enough, on real data, to justify a production investment. That means the evaluation phase is not optional polish, it is the point of the exercise. A demo that looks good on three cherry-picked examples but has never been tested against a representative sample of real questions has not actually proven anything, no matter how impressive it looks in a stakeholder meeting. Build in enough evaluation cycles to test against genuinely difficult and edge-case inputs, not just the happy path.
From PoC to production without starting over
The most expensive mistake at this stage is building a PoC that cannot evolve into a production system, forcing a full rebuild once the pilot succeeds. Architect the PoC with production concerns in mind from the start, even if you deliberately skip building them out: know where authentication, logging, and error handling will need to go, even if week one of the PoC does not implement them fully. A PoC that was built disposable usually gets thrown away, taking weeks of learning with it.
How Netray runs AI proof of concepts
Netray starts every PoC by filing the data access request on day one, in parallel with technical setup, specifically because it is the most common source of schedule slip. We build every PoC on real production data wherever legally possible, with a structured evaluation set defined before the build begins rather than assembled after the demo looks good. For manufacturers, that often means working directly against live SyteLine or LN data under a scoped access agreement rather than a static export.
Frequently Asked Questions
Why does data access take so long even for a simple PoC?
Because it is not a technical step, it is a legal and security review process that runs on its own institutional timeline, often independent of the AI project entirely. A data processing agreement with a new AI vendor can take security and legal several weeks to review, especially if the vendor is unfamiliar or the data involves any regulated category. File this request in the first week of the project, not after the technical team is ready to start, or it becomes the critical path by default.
How many evaluation cycles are actually enough?
Three is a reasonable minimum for most enterprise use cases: an initial baseline test that reveals obvious failures, a second cycle after fixing the most common problems, and a third to confirm the fixes held and did not introduce new issues. Complex or high-stakes use cases, particularly anything customer-facing or safety-relevant, often warrant more. Fewer than three cycles usually means stakeholders are seeing a demo, not evidence the approach works.
Should the PoC use production data or a synthetic sample?
Real production data whenever you can legally get it, because synthetic or curated sample data tends to be cleaner than reality and produces evaluation results that do not survive contact with actual production traffic. If real data access genuinely cannot be arranged within a reasonable timeline, treat that as a serious finding about your organization's data governance, not just a scheduling inconvenience to route around.
What typically happens between a successful PoC and production launch?
A second phase of work that is often underestimated: hardening for real user load, adding proper authentication and access control, building monitoring and evaluation into the ongoing operation, and integrating with production systems rather than a PoC's simplified connections. Budget this as a distinct phase with its own timeline; our AI project cost estimator and PoC to production playbook cover it in more detail.
Get a realistic PoC timeline and a data access plan built for your legal and security review process.
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