A short, fixed-scope AI proof of concept on your own data, so you know if an idea works before you fund a full build.
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We run short, fixed-scope AI proofs of concept and AI MVPs that test an idea on your own data before you commit to a full build.
Success metrics are agreed up front, so the result is a working prototype and an evaluation report with accuracy, cost and latency, not just a demo. You finish with a clear go or no-go decision and, if the idea works, a roadmap to production.
Many AI projects stop at the slide stage because the risk and value are unclear.
Nobody knows if AI can reach the accuracy the process needs until it is tested on real data.
Leadership will not fund a full build without evidence of value, cost and risk.
Vendor demos on clean sample data say little about results on your own documents and records.
Model and infrastructure costs at production volume are hard to estimate without measurements.
Teams are unsure whether their data is complete, clean or accessible enough for AI.
Outcomes
One agreed use case with accuracy, cost and latency targets written down before the build.
Findings on data quality, coverage, access and privacy, with what must change for production.
A prototype your team can try, with a simple interface or API on your own data.
Accuracy, cost per request and latency measured on a held-out test set and compared with a baseline.
A clear recommendation with the reasons, risks and limits we found.
Architecture outline, steps and estimate for the full build if the result is positive.
Test whether AI can pull fields from your invoices, forms or contracts accurately enough to cut manual entry.
Test whether an LLM can answer staff or customer questions reliably from your documents.
Test whether a model beats your current forecast on your own historical data.
Test whether emails, tickets or requests can be sorted and routed automatically.
Test whether a model can flag at-risk customers, transactions or cases early enough to act.
Test an AI feature with a small group of users before adding it to your roadmap.
Built with
We agree what good looks like in numbers before the first line of code, so the result is a decision, not an opinion.
If the idea does not work on your data, the report says so and why. A clear no at PoC cost is cheaper than a slow yes.
Cost per request and the estimated monthly running cost are part of the evaluation, alongside accuracy and latency.
You see the prototype working on your own material, then receive an architecture outline and estimate for the full build.
Prototype code and evaluation sets are yours, under NDA, whether or not you build with us.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We review your idea, the business problem and the data available, and agree one use case to test.
You receive Agreed use case and scope
We check a sample of your data, prepare a test set and agree the accuracy, cost and latency targets that define success.
You receive Data readiness note and success criteria
We build a working prototype with the simplest approach likely to meet the targets, using hosted models or your own environment.
You receive Working prototype
We test the prototype on held-out data, measure accuracy, cost and latency and compare it with the current process.
You receive Evaluation results
We demo the prototype to your stakeholders, walk through the results and the limits we found.
You receive Stakeholder demo
We recommend whether to proceed and, if so, set out the architecture, effort and steps to a production system.
You receive Evaluation report and production roadmap
Tech stack
AI and machine learning
Backend and APIs
Languages
Cloud and DevOps
Other tools
Next step
If you need a complete web or mobile product to take to market, our MVP development service covers design, build and launch.
A PoC is a fixed-scope engagement, so you know the cost before we start. The price depends on the number of data sources, how much data preparation is needed, the type of model or approach and the level of evaluation required. Model and cloud usage during the PoC is usually small and is estimated up front. As a guide, a fixed-scope AI proof of concept usually costs $10,000 to $50,000 (USD).
Most proofs of concept take a few weeks, depending on data access and scope. We agree the timeline in the discovery workshop and keep the scope fixed so it does not grow.
A PoC answers whether the AI approach works on your data, measured against agreed metrics. An AI MVP goes one step further: a small product or feature real users can try, with a basic interface and integration, so you can test value as well as feasibility.
A representative sample of the data the system will use, such as documents, records or historical outcomes, plus some examples with known correct answers for testing. We help you prepare the test set if you do not have one.
That is a useful result. You learn it in a few weeks instead of after a full build, and the report explains why: data quality, model limits or cost. Often it also points to a narrower use case or a different approach that does work.
A working prototype, the code, the test set and an evaluation report with accuracy, cost and latency results, a go or no-go recommendation and, if positive, a roadmap and estimate for production.
We work in your cloud account or an isolated environment, use only the data the PoC needs, mask personal data where possible and use enterprise model services whose business terms do not use your data for training. Data handling is designed around GDPR and the local data protection rules that apply to you.
You own the code and IP we build for you, including the prototype, prompts, models and test set. You can take them to production with us, your own team or another partner.
Yes. We can run the PoC alongside your data, product or engineering team, work in your repositories and share findings as we go so your team can carry the work forward.
We can build the production system, harden the prototype, add integrations, monitoring and security, or support your team while they build it. The roadmap from the PoC sets out the steps and effort.
Book a call, and we pick the use case, agree the success metrics and list the data needed. Most PoCs start within a week of the NDA and data access being in place.
Book a free consultation. We will review your idea and data and suggest a fixed-scope proof of concept.
