How do you evaluate and select the right AI models for specific use cases?

We start with the problem, not the model. Define what the AI step needs to do, what accuracy is required, and what constraints exist. Then test candidate models against your actual data.

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The evaluation covers four dimensions. Accuracy on your data: a model scoring well on public benchmarks may underperform on your specific documents or formats. Latency: if you need to process hundreds of items per second, a large model may be too slow. Cost: API-based models charge per use, which adds up at volume. Compliance: if data cannot leave your environment, cloud-only models are off the table.

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Roborana gathers 100 to 200 representative samples from your process, tests multiple models against them, and compares results. If several models perform similarly, we pick the cheapest or fastest. If one clearly outperforms, we accept its cost.

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After deployment, we monitor for drift. Performance can degrade when new data differs from what the model was trained on. Automated monitoring catches this early so we can retrain or switch models before quality drops.