AI, Data & Intelligence
AI Proofs of Concept
Time-boxed experiments, usually four to six weeks, that test whether an AI idea works on your real data and processes, with success criteria agreed up front and an honest go or no-go recommendation.
Capability overview
What ai proofs of concept involves
A proof of concept should answer one question cheaply: is this AI idea worth building properly? Too often PoCs become open-ended demos judged on how impressive they look. We fix the duration, the data, the success measures and the decision that will follow before any work starts.
Experiments use a representative sample of your real documents, records or conversations, including messy and difficult cases, because accuracy on clean examples says little about production. The final report presents measured results, failure analysis, estimated running costs and what a production build would require.
What is included
What a proof of concept includes
Success criteria
Measurable thresholds agreed in advance, such as extraction accuracy above a set level or a share of test questions answered correctly with citations.
Representative test set
A labelled sample drawn from real operations, including edge cases, reviewed by the people who do the work today.
Working prototype
A functional prototype users can try, built quickly but on the same kind of models and data that production would use.
Cost and risk estimate
Projected running cost at expected volumes, plus security, privacy and operational risks identified during the experiment.
Decision report
Results against criteria, failure analysis and a recommendation to proceed, pivot or stop, with a production estimate if relevant.
How we work
How we deliver ai proofs of concept
Week one: framing
Use case, criteria, data access and test set agreed, and a baseline measured for the current manual process.
Weeks two and three: build
Candidate approaches implemented and compared, with the most promising refined against the test set.
Week four: user trial
A small group of business users tries the prototype on real tasks and records where it helps or falls short.
Weeks five to six: evaluation
Final measurements, cost modelling and failure analysis completed and written up.
Decision meeting
Results presented to sponsors with a clear recommendation and, if positive, a scoped plan for production.
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Questions & answers
Questions about AI Proofs of Concept
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionThat is a useful outcome. A clear negative result after six weeks costs far less than a failed production project. The report explains why, and whether a different approach or better data could change the answer.
Usually not directly. PoC code prioritises speed of learning over security, scalability and maintainability. Parts such as prompts, evaluation sets and data pipelines are often reused in the production build.
A representative sample, often a few hundred to a few thousand items, provided in a secure environment. Personal data can be masked or processed within your own cloud tenancy.
Each proof of concept is a fixed-price, fixed-duration engagement agreed after a short scoping call, so the cost of finding out is known in advance.
Next step
Discuss ai proofs of concept with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.