Perceptron ML

Start with one narrowly scoped workflow that matches Perceptron ML's main job. Provide only the files, account connections, data, or context needed for that workflow and define what a correct outcome looks like before delegating work.

Run several representative cases, including at least one difficult or incomplete case. Check factual accuracy, source grounding, calculations, extracted fields, generated artifacts, and any actions proposed by the system. Review logs or traces when available rather than evaluating only the final answer.

For systems connected to business, financial, healthcare, customer, or production data, use least-privilege access and keep a human approval step for consequential actions. Test failure handling, escalation, and what happens when an integration or upstream data source is unavailable.

Once the workflow is dependable, standardize reusable instructions, policies, templates, integrations, or evaluation cases. Re-test after significant product or model updates because capabilities and behavior can change.