Across recent industry data, 88% of AI pilots never reach production. For every 33 POCs, only four ship. Not because the AI doesn't work. Because nobody planned for what happens after the demo. We build proofs of concept designed to answer one specific question on your real data, in your real infrastructure, at a cost that makes business sense. Production architecture from day one, so if the answer is yes, the code carries forward to the build instead of being thrown away.
Pixelfield is for CTOs, Heads of Product and innovation leads at companies that need to validate an AI use case before committing the larger budget. The seniors who scope the POC also write the code. 50+ AI features in production, AgentWise was a POC before it was a product, and a reputation for telling clients to stop when the POC says don't build.
Out of pilot purgatoryProduction architecture from day oneFixed scope, fixed price, fixed timelineHonest go / no-go with evidence
An AI feature can be demoed in three weeks. Making it reliable, auditable and cost-predictable at scale is a different problem. Demo-theatre POCs use clean test data, skip integration, ignore cost at volume and impress the board with something that can never ship. We build POCs against your real data and your real systems so the demo and the production system are the same shape, just different scope.
Most consultancies sell a 'POC' that's really an assessment report and a roadmap. Months of effort, hundreds of pages, no working system at the end. We do the opposite: a working system on your real data, a short report that answers the one question you scoped, and code you can take into production with the same engineers. The deck is the by-product, not the deliverable.
The biggest hidden cost of a bad POC is the rewrite. If the POC code is throwaway, you pay for the validation and then pay again for the production build that starts from scratch. We design POC architecture to be a smaller, simpler version of the production system, not a different system. Same auth model, same data layer, same evaluation harness. The build is an extension of the POC, not a replacement.
Around 42% of companies have scrapped most AI initiatives. The POC is supposed to surface that early, not at month nine of a six-month build. We've recommended 'don't build' on POCs where the data wasn't ready, where the cost model didn't work, where a rules engine would have been cheaper and more reliable. That's the POC paying for itself by saving you a much larger mistake.
Before any contract is signed we sit down with you and write the POC question in one sentence: 'Can [specific AI capability] reach [specific accuracy or business metric] on [specific dataset] inside [specific cost envelope]?'. We surface the assumptions, the data the POC needs, the systems it has to integrate with and the threshold for go/no-go.
If the question can't be written in one sentence, we won't take the engagement.
Build runs as a small senior team led by Michal Vavra. We stand up the POC on production architecture against your real data: data ingestion, model and prompt or pipeline, evaluation harness, integration scaffolding, cost telemetry, monitoring stub. We run the system on your real corpus or traffic and measure against the metric defined in scoping.
Code is yours from day one. Same engineers will carry it into production if you greenlight.
We run the system against the failure modes that usually kill production AI: edge cases in real data, integration friction with your stack, cost at projected volume, latency budget, security and compliance review. Failures here are the point. They're cheaper to find now than at month four of a build.
Output: an evaluation report sliced by data type and condition, an integration friction list, a projected run-rate at production volume, and a list of the specific risks that would need to be designed against in a production build.
We deliver the POC with a clear go / no-go recommendation backed by evidence, the architecture we'd build for production, a fixed-scope quote for the build, and the projected monthly run-rate. If the answer is yes, the same team takes the POC code into the production engagement. If the answer is no, you keep the report and walk away with a clear understanding of why and what would have to change.
Most clients move into a production build inside a fortnight. Some take the report and execute internally. Either is fine.
Direct answers to the questions CTOs and Heads of Product ask us on every <strong>POC scoping call</strong>.
Light POCs (single feature, single data source) start from £8K and run two to four weeks. Standard POCs with real-data testing, integration on your stack and a cost model at production volume typically land £15K-£30K over four to six weeks. Fixed scope, fixed price, fixed timeline. Scoping call is free, no contract until we've written the POC question down together.
Most engagements run four to six weeks from kickoff to a delivered report and working system. Light POCs can land in two to three weeks where the data layer is already clean and the integration surface is small. The hardening pass at the end (weeks four to six) is where we surface the failure modes that usually kill production AI. We don't recommend skipping it.
POC answers a feasibility question: will this AI approach work on our real data, in our real stack, at our cost? MVP delivers a working system to real users at small scale to validate willingness to pay or operational fit. POC is for the build/no-build decision. MVP is for the pilot/scale decision. They're stages on the same path. We do both, and we'll tell you in scoping which one your situation actually needs.
That's a successful POC. The point is to find out in weeks, not months. We've delivered POCs where the recommendation was 'don't build the AI feature, fix the upstream data layer first' or 'a rules engine would be cheaper and more reliable here'. You walk away with a defensible technical report, the architecture we'd have built, and clear evidence for the call. The POC paid for itself by avoiding a much larger mistake.
Yes to both. Standard contract is full IP ownership for the client: source code, prompts, evaluation datasets, infrastructure-as-code, monitoring scaffolding, the technical report and the architecture proposal. The POC is built on production architecture (different scope, same shape) so the code carries forward into the production build. Same team, no rewrite, no 'phase two starts from scratch' tax.
We sign an NDA before any data leaves your environment. Where data residency or compliance requires it, we run the POC inside your VPC or on-prem rather than on our cloud. We support Azure OpenAI with zero retention, private deployments on AWS and GCP, and self-hosted open-source models. UK GDPR, DPA 2018 and sector frameworks (FCA SYSC, NHS DSPT, PCI DSS) are covered as standard.
Skip the POC if you've already validated the use case on real data, the integration surface is well understood and the cost model is clear. In that case the POC adds time without adding evidence. We'll say so in the scoping call and point you at our AI Development Services instead. We'd rather not run a POC than run one that doesn't earn its place.
