AI Proof of Concept Development

What an AI proof of concept should prove

An AI proof of concept is a short, contained build that answers one question you cannot answer on paper: can a model do this specific task, on your data, well enough to be useful? It is not a pitch demo and it is not a first release. Tepia treats it as an experiment with a hypothesis, a method and a result that might be no.

Good questions for a proof of concept are narrow and testable. Can a model pull the policy number, effective date and coverage limits from scanned declarations pages? Can it sort warranty claims into your twelve defect codes as reliably as a trained clerk? Can it answer technician questions from a 400 page equipment manual without inventing a torque value? Each has a right answer someone on your team can check.

Questions that make poor proofs of concept are the vague ones, like whether AI can improve customer experience. Tepia narrows those in the first conversation, often by working backward from a step in your process where staff read, sort or retype information all day. If you are still deciding which question matters most, start with an AI readiness assessment.

Proof of concept, prototype and MVP compared

Tepia uses all three, and mixing them up is one of the most common reasons AI projects lose months. Each answers a different question:

Teams that blur these end up with a polished interface sitting on an untested model, or a strong model with no idea whether users want it. Tepia sequences them deliberately. When model risk is high, the proof of concept comes first. When the AI part is well understood, Tepia may go straight to a prototype or an AI MVP.

Setting pass or fail criteria before writing code

The most useful thing Tepia does in a proof of concept happens before any model is called. Together with your subject experts, Tepia assembles an evaluation set: a few hundred real examples, each paired with the answer a skilled person would give. That set becomes the exam the AI has to pass, and it keeps everyone honest about results.

Criteria are written down and signed off, typically covering:

• Accuracy: the share of examples the AI gets right, measured against the human baseline rather than an abstract target.
• Failure behavior: what the AI does when it is unsure, and whether it says so instead of guessing.
• Speed: how long a response can take before the workflow breaks, which differs between a live chat reply and an overnight batch.
• Data handling: which fields may be sent to a model provider and which must be masked or stay inside your cloud account.

Tepia then tries more than one approach against the same set, for example a plain prompt, a retrieval step over your documents, and a second model provider. Comparing them on identical examples shows what actually moves the score. Our LLM evaluation and testing page goes deeper on building these test sets.

How Tepia approaches AI proofs of concept

Tepia compresses its six phase process for a proof of concept, keeping every phase but sizing each one to the question being tested. A US based project manager keeps the scope from creeping into a full product.

1. Discovery: agree on the single question, collect sample records and review data access and privacy rules. Deliverables are an Investigation Summary, an Interview Summary with your subject experts, and User Stories describing the task as a person does it today.
2. Design: define the evaluation set, scoring rules and success thresholds. When users will see output, Tepia adds simple wireframes of how results and uncertainty would appear.
3. Development and Testing: build the pipeline, run each candidate approach against the evaluation set, and record results run by run. Test plans cover functional accuracy and non functional checks such as response time.
4. Training: a walkthrough for your reviewers on reading results, spotting failure patterns and adding new test cases.
5. Launch: the readout, with scores, examples of successes and failures, and a go or no go recommendation with the path to production.
6. Support: if you proceed, the evaluation set and connectors move into the MVP build and keep running as regression tests.

A proof of concept fits inside the usual Discovery window of 1 to 3 months, and moves faster when data access is ready on day one. Read more about our process.

Why AI proofs of concept stall, and how to avoid it

Gartner’s forecast that a large share of generative AI projects would be dropped after the proof of concept stage matches a pattern Tepia sees often. The demo works, everyone is impressed, and then nothing ships. The causes are usually predictable:

• Hand picked examples: the demo used clean documents, while production inputs are blurry scans, forwarded emails and partial records.
• No path to integration: the experiment ran on exported files, and nobody checked whether the source system has an API.
• No owner: the work belonged to an innovation group, and the operations team that would use it never asked for it.
• Unclear value: the AI worked, but no one measured how long the manual task takes today, so there is no case for the next step.

Tepia designs against each of these from the start: realistic samples, an integration check in Discovery, an operations owner in every review, and a baseline measurement of the current process. Projects that pass usually continue into a production feature through our AI integration services. To scope a proof of concept on your own data, contact Tepia.

Frequently asked questions