AI Proof of ConceptDevelopment
Find out quickly and cheaply whether an AI idea works on your own data, with measurable criteria agreed before a line of code is written.
AI Proof of Concept Development: what the work involves
AI ideas are easy to demo and hard to trust. A slick presentation on three hand-picked examples says little about how the system behaves on the messy reality of your files, your customers and your edge cases. Companies commit to full builds on the strength of a demo, then discover that accuracy, cost or latency never reached what the business case assumed, and by then the budget is spent.
DevKey structures the proof of concept like an experiment. First we write the hypothesis and success criteria with you: what the system must do, how it will be measured, and what result means stop. We gather a representative sample of real data, build the thinnest prototype that exercises the risky parts, and test it against a held-out set. The deliverable is a working prototype, an evaluation report with failures shown honestly, cost and latency estimates, and a recommendation on whether to proceed, change approach or stop.
Core features
Hypothesis and success criteria
We agree in writing what the prototype must achieve, how it is scored, and which results would justify moving ahead or stopping.
Representative data sampling
A sample is chosen to include the awkward cases, such as poor scans, mixed languages and rare categories, so results are not flattered.
Thin-slice prototype
We build only what is needed to test the riskiest assumptions, whether that is extraction accuracy, retrieval quality or response speed.
Held-out evaluation
Results are measured on examples the system never saw while being tuned, with errors categorised to show why they happen.
Cost and latency estimates
Cost per request, response time and infrastructure needs are measured and projected to realistic volumes.
Go, adapt or stop recommendation
You receive a plain recommendation, the risks of each path and, if you proceed, an outline of the production build.
What we get right before launch
A proof of concept is not a product
Prototypes skip authentication, scaling, monitoring and edge-case handling. We label them clearly and list what production would add, so a successful demo is not mistaken for a finished system.
Sample bias
If the test data is easier than live data, results mislead. We involve the people who handle the real work in choosing samples, and report confidence honestly given the sample size.
Stopping is a valid outcome
Sometimes the evidence says the idea is not ready or not worth it. We would rather tell you that after a short experiment than after a long build, and the learning still has value for the next attempt.
Tools and technology
- Python
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- LangChain
- Streamlit
- FastAPI
- pgvector
- Label Studio
Common questions, answered
How long does a proof of concept take?
Usually a few weeks, driven mostly by how quickly we get access to representative data. Clear scope matters more than duration, so we fix the question to be answered and avoid creeping features.
What do we need to provide?
A named business owner, access to sample data, and a few people who know the real process and can judge outputs. Sensitive data can be handled under agreed controls or anonymised first.
What happens if the proof of concept fails?
You still receive the evaluation report and the reasons. Often it points to a smaller scope, better data or a different method. Knowing early what does not work is part of the value.
Can the prototype become the real product?
Sometimes parts can be reused, but we often rebuild for reliability and security. We state which components are reusable in the final report, so your production estimate is realistic.
More AI Agents & Automation services
All AI Agents & Automation servicesAI Strategy Consulting
Cut through the hype with a short, evidence-based plan that ranks where AI can help your operation, what it needs, and what to skip.
AI Integration for Existing Software
Add AI to the product or internal system you already run, through clean service boundaries and a gradual rollout, with no rewrite and no big-bang release.
Prompt Engineering and Evaluation
Replace trial-and-error prompting with versioned prompts and a test suite, so every change is measured and regressions are caught before users see them.
Ready to start your AI Proof of Concept Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.