What a Proof of Concept Is
A proof of concept is a small-scale, focused technical implementation that demonstrates whether a specific approach is feasible. Unlike a UX prototype, it involves working code and real technical components. Unlike an MVP, it is not user-facing, does not have production-quality error handling or authentication, and is not intended to be shipped as a product. It exists to answer a specific technical question with a real implementation. For an AI product, the PoC might demonstrate that a RAG pipeline can answer questions about the client's specific document corpus with sufficient accuracy, that a vision model can detect the required objects in images from the client's actual data source, or that a specific LLM can classify support tickets according to the client's taxonomy with an accuracy that meets the business threshold. The PoC is often a script, a notebook, or a minimal web service. Its code may be thrown away or rewritten to production standards in the MVP phase. The output of a PoC is not the code, it is the answer to the feasibility question.
When to Build a PoC vs Going Straight to MVP
Not every project needs a PoC. If your technical approach is well-established and the uncertainty is in market demand rather than technical feasibility, going straight to MVP is the right choice. You need a PoC when the technical approach is novel or unproven for your specific use case, when the feasibility of achieving the required accuracy or performance threshold is genuinely unknown, when the success of the MVP depends critically on a specific technical capability, or when the PoC can be completed significantly faster and cheaper than building a full MVP. A PoC is particularly valuable when you are considering AI approaches that involve proprietary data. An LLM's performance on your specific domain, with your specific documents, your specific vocabulary, and your specific edge cases, cannot be reliably predicted from benchmark results on general datasets. Testing it on a sample of your real data before committing to the full build is cheap insurance against discovering fundamental limitations after significant investment.
Structuring an AI Proof of Concept
A well-structured AI PoC has four components. First, a clear success criterion defined in advance: the specific accuracy, latency, or quality threshold that would constitute a positive result. This prevents post-hoc rationalisation of mediocre results. Second, a representative test dataset that reflects the real distribution of inputs the system will face in production, including edge cases and examples from the harder end of the input spectrum. Third, the minimal implementation required to test the approach, typically a script or notebook that runs the approach against the test dataset and measures the defined success criterion. Fourth, a structured failure analysis: if the approach does not meet the threshold, understanding why it failed informs what alternative approach to try. A PoC that clearly fails is as valuable as one that succeeds, because it prevents a larger investment in an approach that was not going to work.
PoC to MVP: What Changes
The PoC answers whether you can build it. The MVP answers whether people want it. The transition from PoC to MVP involves several upgrades. Production-quality error handling replaces the PoC's assumption of clean inputs. User authentication and access control are added. The AI component is wrapped in a user-facing interface. Logging and monitoring are implemented. The code is refactored from script-level to application-level quality with tests. Depending on what the PoC revealed, the AI approach itself may be refined: the chunking strategy adjusted, the prompt improved, the retrieval parameters tuned. The PoC's evaluation dataset often becomes the seed of the MVP's ongoing evaluation suite, providing a baseline for measuring quality changes as the product evolves. The cost of the MVP builds on the PoC result: if the PoC showed the approach works but needs 20% more accuracy, the MVP scope can include the additional prompt engineering or retrieval tuning required to achieve that threshold.
PoC in Regulated Industries
In UK regulated industries, proofs of concept serve an additional purpose beyond technical validation: they generate the evidence required for regulatory engagement. For healthtech products that may need MHRA scrutiny as medical devices, a PoC demonstrating technical performance on a relevant clinical dataset provides the foundation for a regulatory submission. For fintech products seeking FCA authorisation, a PoC that demonstrates model behaviour on relevant financial scenarios supports the technical documentation required for the authorisation process. NHS Digital Data Access agreements, required for PoCs that use NHS patient data, have their own governance process that must be started well in advance of the PoC itself. For products in scope of the EU AI Act's high-risk category, PoC evidence feeds into the technical documentation and conformity assessment process. Planning the PoC with regulatory evidence requirements in mind from the start avoids the need to reconstruct documentation after the fact.
SpeedMVPs PoC Engagements
SpeedMVPs offers scoped PoC engagements for founders who need to validate an AI approach before committing to a full MVP build. A typical PoC engagement runs 3-5 days and includes: success criterion definition, test dataset preparation guidance, the minimal implementation against your actual data, a results report with failure analysis, and a recommendation on whether to proceed to MVP and with what adjustments. For clients who PoC successfully and proceed to MVP, the PoC evidence and evaluation dataset transfer into the MVP delivery phase. All code produced during the PoC is owned by the client. For clients who PoC and find the approach is not feasible, SpeedMVPs includes a recommendation on alternative approaches as part of the PoC output. This saves clients from discovering feasibility issues after investing in a full MVP build. MVP projects from GBP 8,000, 2-3 week delivery.