Fine Tuning
-
We collect curated transcripts, emails, chat logs, and process documents that reflect correct workflows, escalation rules, tone standards, and factual accuracy. Remove noise, outdated content, and anything that would teach the wrong behavior.
-
Real CX/SDR scenarios are converted into supervised training examples: customer question [to a] ideal agent response, lead objection [to a] qualified reply, data request [to a] compliant refusal, ensuring each example reflects best practices and client policies.
-
Examples for things like lead-qualification, troubleshooting, objection handling, quote workflows, compliance boundaries, and refusal scenarios are included so the model performs consistently across the full domain-specific dataset.
-
After fine-tuning, the model is tested against benchmark scenarios, and SMEs verify correctness, flag errors and refine the dataset. Fine-tuning cycles are re-run until outputs consistently meet quality, safety, and compliance standards.
