Proof of Concept & Pilot
A short build with real data that proves or kills an idea before serious budget is committed.
A proof of concept answers one question: does this work well enough, on our data, to be worth building properly? We define the question, build the smallest system that can answer it and evaluate the result against criteria agreed in advance. It takes weeks, not quarters.
A pilot goes one step further, putting the system in front of a small group of real users to measure adoption and value before scaling.
- Success criteria are fixed before the build, so the result can't be argued into a yes.
- We build on the components the production system would use, so a yes doesn't mean starting over.
- The evaluation set is a lasting asset for every future version.
- A group with a ranked list of AI opportunities and a steering committee that funds on evidence.
- A bank or insurer that must show a model works on its own data before procurement can begin.
- An operations-heavy business, such as a logistics group or an airport, testing whether AI can handle a specific decision.
- A vendor demo impressed everyone and nobody has seen it on your data.
- A large build is proposed on an untested assumption.
- Two teams argue about feasibility and neither has measured it.
- The budget gate needs a number, not a narrative.
What is included.
- 01
Success criteria
The question, the metrics and the thresholds that would justify a full build, agreed before work starts.
- 02
Data preparation
A representative dataset assembled and cleaned enough to give an honest result.
- 03
Build
A working proof of concept using production-grade components where possible.
- 04
Evaluation
Measured against the criteria with an evaluation set you keep.
- 05
Pilot
Optional deployment to a small user group with usage and feedback tracked.
- 06
Go/no-go memo
Results, costs to productionise and a clear recommendation.
Four steps, no surprises.
- 01
Define
Question, criteria, data and scope in a one-week setup.
- 02
Build
The proof of concept constructed in one to three weeks.
- 03
Evaluate
Results measured and, where relevant, piloted with users.
- 04
Recommend
Memo with the decision and the plan for what follows.
From first meeting to steady state.
- 01Week 1
Define
Question, criteria, data and scope fixed in writing.
- 02Weeks 2 to 4
Build
The smallest system that can answer the question, on production-grade components where possible.
- 03Weeks 5 to 6
Evaluate and recommend
Results measured on the evaluation set, piloted with users where relevant and a go/no-go memo issued.
- Result against the success criteria agreed before the build.
- Adoption and feedback from the pilot user group.
- Estimated effort to reach production.
- Decision reached at the gate on evidence.
- AI engineering lead
- Machine learning engineer
- Data engineer
- Product analyst
- Success criteria document.
- Working proof of concept.
- Evaluation set and results.
- Pilot usage report where applicable.
- Go/no-go memo with production estimate.
Proofs of concept are fixed scope and run two to four weeks. Pilots add two to six weeks depending on user group and integration needs.
AI Strategy & Discovery Workshop
Map where AI creates measurable value in your data and processes, and leave with a ranked roadmap.
Web, Webshop & App DevelopmentMVP & Rapid Prototyping
Validate an idea in weeks with a clickable prototype or a thin slice of production software.
AI Consulting & AutomationCustom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
Proof of Concept & Pilot, in plain terms.
A demo shows what is possible. A proof of concept measures what is achievable on your data against agreed criteria. Only one of them supports a budget decision.
Yes, with appropriate controls. Synthetic data produces misleading results for most business problems.
Usually not without hardening, but the core can be reused. The memo lists what production needs.
Start with the discovery workshop, which produces the ranked list this service tests.