AI proof of concept on your real data with one agreed success metric
Before you fund full development, we test the hypothesis on your real data. One task, one metric, an honest report. You see how AI handles your calls, documents or camera footage, not a vendor sample.
What hurts
The vendor demo looks great, but your data is different. Mixed languages, noisy recordings, non-standard documents. You do not know whether it will work for you.
Management will not approve a full build without evidence. They want numbers from the company’s own data, not case studies from other businesses.
You worry that a pilot turns into an endless project. You need clear limits: what we test, which metric counts as success and when the decision is made.
What we build
- Hypothesis and a single success metric with a threshold
- Collection and labelling of a sample of your real data
- Baseline: how the task is done today
- Prototype on several models, including our own hosted LLM
- Test cases for objective scoring
- Error analysis: where and why the model fails
- Model running cost estimate at your volume
- Report with a recommendation: go, change approach or stop
What you get
- Working prototype your team can try
- Labelled test dataset
- Report with metric results and error analysis
- Cost and timeline estimate for full development
- Prototype code in your repository
How we measure the result
- Value of the agreed metric on the test set
- Difference against the current process baseline
- Error types and how often each occurs
- Model cost per processed item
Systems and stack
Industries
Questions
How much does an AI proof of concept cost?
From $5,000 for 3–5 weeks. The price depends on the data type and labelling volume. Text and calls are usually quicker; camera footage needs more preparation. If your data is already collected and accessible, the PoC moves faster.
What is a good success metric?
One tied directly to money or time. For example, the share of invoices processed without manual correction, or the share of calls where the model labels the outcome correctly. We agree the metric and threshold before the start so the result is not open to interpretation.
Can our data stay out of OpenAI and other clouds?
Yes. We run our own LLM on vLLM on our servers, so data does not go to third-party models. Dedicated servers are available for banks and clinics. We compare our hosted model with cloud models so you see the trade-off.
What if the PoC misses the threshold?
You get a report on why: too little data, poor quality, or a task beyond current models. That is still a result, as you have not spent a full development budget. Sometimes we suggest a different approach worth testing.
How does a PoC move to production?
After a successful PoC we give a fixed estimate for full development. Launch goes to part of the traffic first, then to full volume. Test cases from the PoC become the regression suite for further work.
Related services
More in this section
Describe the task. We reply within one business day, the first call is with the founder.
Describe the task. The first call is with the founder.
We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.