Test your AI idea with minimal risk. Our proof of concept service validates whether a solution works before you commit to full development.
Some AI ideas look promising on paper but fail in practice. A proof of concept lets you test whether a solution actually works before committing significant resources. We build focused prototypes that answer the questions that matter.
AI projects carry uncertainty. Technology may not perform as expected. Data may not support intended use cases. Users may respond differently than anticipated. A proof of concept reduces these risks by validating assumptions early.
Running a PoC costs a fraction of full development. If problems emerge, you have lost weeks, not months. If the concept proves out, you proceed with confidence and evidence to support the investment.
Every proof of concept addresses specific questions. These typically include:
Technical feasibility: Can AI technology deliver the required capability? Does it perform accurately enough? Does it handle edge cases appropriately?
Data viability: Does available data support the intended application? Is it sufficient in volume and quality? Can it be accessed practically?
User acceptance: Do real users find the solution valuable? Does it fit their workflows? What friction exists?
Business case validation: Do early results support projected benefits? What refinements would improve outcomes?
We design proofs of concept to maximise learning while minimising investment.
Scoping defines what the PoC will test and what success looks like. We agree specific questions to answer and metrics to measure. This prevents scope creep and ensures the PoC delivers actionable conclusions.
Rapid build creates a working prototype. We cut corners that do not affect the test: rough interfaces, limited scalability, manual processes where automation would come later. The goal is learning, not production quality.
Controlled testing puts the prototype in front of real users with real scenarios. We observe carefully, gather feedback, and measure outcomes against success criteria.
Evidence pack documents what we learned. You receive a clear recommendation: proceed to full development, modify the approach, or stop. Supporting evidence backs up the recommendation.
Most PoC projects run for two to four weeks. We can compress timelines for urgent needs or extend them for complex concepts.
Scope typically includes:
Core functionality for the critical assumptions. Enough capability to test what matters, without production polish.
Representative scenarios. A realistic set of use cases and edge cases that reflect how the system will be used.
Targeted integration (where relevant). Connection to one or two key systems if integration is central to the concept.
Pilot testing. Hands-on evaluation with a small user group to validate fit and friction.
Measurement against agreed metrics. Evidence tied to success criteria, not subjective impressions.
A proof of concept is not a production system. It deliberately takes shortcuts to move quickly. If the concept proves out, further work is needed to build something robust, scalable, and supportable.
We are clear about this upfront. PoC investment buys learning and risk reduction, not a finished product.
Consider a proof of concept when:
You have a promising AI idea but uncertainty about whether it will work. You need evidence before committing to build.
Stakeholders need proof. You need something concrete to support a go/no-go decision.
Technology selection depends on testing. You want to compare approaches before making a platform commitment.
You want to validate assumptions early. You would rather learn quickly than discover constraints late in delivery.
Use these prompts to sharpen scope and define what the PoC must prove.
“What are the key assumptions we need to validate first, and what evidence would change our decision?”
“What test set and success criteria should we use to measure accuracy and failure modes honestly?”
“What are the likely risks in production (data drift, edge cases, governance), and what guardrails would reduce them?”
A working prototype (scoped to the key questions), a summary of findings, and a clear recommendation: proceed, change approach, or stop.
Sometimes, but usually not directly. PoC code often needs rebuilding for production use. However, learnings and designs carry forward, so the investment is not wasted.
Then you have saved the cost of a failed full project. A negative result is still a useful result. We document why the concept did not work and what alternatives might succeed.
We agree what the PoC will and won’t do, define success criteria upfront, and explicitly trade off polish and scalability in favour of learning.
Feasibility validates an idea through analysis and targeted experiments. A PoC builds working software to prove the concept in practice with real scenarios.