AI proof of concept. Evidence first.

How to build an AI proof of concept that answers one clear question

An AI proof of concept is a small, focused build that tests whether AI can do a specific job well enough on your data. It is not a product and not a demo for a board meeting. Its only purpose is to answer a question that would otherwise make a larger investment a gamble.

That question should be narrow. Can a model extract the right fields from our supplier invoices? Can it answer customer questions correctly from our product documentation? Can it sort incoming requests into the right categories? A question like this has an answer you can measure, and that is what makes the proof of concept worth doing.

The rest of the work follows from the question: collect real examples, decide what good enough means, build the smallest thing that runs the task, and measure. If the result is strong, you move on with confidence. If it is weak, you have learned that cheaply.

• Define one task the AI must perform, in a single sentence.
• Collect real examples of that task, including the awkward ones.
• Write down what a good result looks like and how you will measure it.
• Build the smallest working version that runs the task on those examples.
• Measure, review the mistakes and decide what happens next.

Why an AI proof of concept is different from a demo

Almost any AI idea looks good in a demo. A model answers a few well-chosen questions fluently, everyone is impressed, and the project gets approved. Then it meets real data, with its typos, odd formats and edge cases, and the results are less convincing.

A proof of concept protects you from that gap. It runs on a realistic sample of your own material, not on examples picked to look good. It counts the failures as carefully as the successes, and it looks at what kind of mistakes the AI makes, not only how often.

That distinction matters most when a wrong answer has a cost. An assistant that drafts internal notes can afford the occasional slip. A system that posts orders or answers customers needs to be measured properly before anyone relies on it.

Setting success criteria and preparing test data

Write your success criteria before you see any results. Once numbers come in, it is very tempting to move the bar. Decide in advance what share of cases must be handled correctly, which mistakes are acceptable and which are not, and how fast or affordable each answer needs to be.

Good criteria compare against today. If your team handles a task with a certain level of accuracy and speed, that is the baseline the AI has to meet or support. Sometimes the right goal is not full automation but handling the easy cases well and passing the hard ones to a person.

The test set is the heart of the proof of concept. Gather a few dozen to a few hundred real examples, depending on how varied the task is, and record the correct answer for each one. Include the difficult cases on purpose: they are where the real answer to your question lives.

• Accuracy: what share of cases must be correct to be useful?
• Mistakes: which errors are tolerable, and which are unacceptable?
• Uncertainty: can the system say when it is not sure?
• Speed and running cost: what is acceptable per task?
• Data handling: where may your data be processed and stored?

Running the proof of concept and reading the results

Build only what the test needs. A simple flow that takes each example, runs it through the model with your instructions, and records the output is often enough. Screens, accounts and integrations can wait: they add time without changing the answer to your question.

Try more than one approach when it is cheap to do so. Hosted models from the major AI providers and open models that run on your own hardware can behave quite differently on the same task, and small changes to instructions or to how documents are prepared can matter as much as the choice of model.

When the results come in, read the mistakes, not only the score. Errors that cluster around one document type or one kind of question point to a fix. Errors that are scattered and unpredictable suggest the task may not be ready for AI yet, or needs a person in the loop.

What to do after your AI proof of concept

A proof of concept ends with a decision. If the results meet your criteria, the next step is a production version: connected to your systems, with logging, monitoring and a review step where it matters. The test set you built becomes the check that every later change must pass.

If the results are close, adjust and test again: better instructions, better document preparation, a narrower task or a review step for uncertain cases. If they are far off, stopping is a valid and valuable outcome. It is far cheaper to learn that from a small test than from a finished system nobody trusts.

How Walhallah builds AI proofs of concept

We start by helping you sharpen the question and the success criteria, then build a focused proof of concept on your own examples. With AI at the core of how we build, that is often ready in days, so you see real results while the decision is still open.

You get the results and the reasoning behind them: what worked, where it failed and why, and a plan for what is next. When the evidence supports it, that plan leads to a production build, usually in weeks rather than months. When it does not, we say so plainly.