Transcripts, emails, chats, and call outcomes are gathered; filtering out irrelevant chatter, incorrect agent responses, unverified claims, and outdated processes so accurate, high-quality examples remain.

We annotate examples with precise intents (billing issue, product question, quote request, lead qualification, tailored email), correct next steps, escalation logic, and “gold standard” responses so domain-specific models can learn the right behavior.

We mark scenarios where agents, or the model, tend to invent information (pricing, compliance claims, troubleshooting steps not supported by the product). The correct allowed boundaries are defined so the model knows what it must not guess on.

As domain-specific datasets change, CX/SDR agents can audit and retrain to match new product features, revised SOPs, changes in compliance rules, or updated customer messaging. This prevents a model from learning outdated or incorrect patterns.