Behavioral datasets teach a model how real people actually act — not just what they say. Wisfer lets you customize those signals, fine-tune on them, schedule A/B tests and batch retraining, and pinpoint exactly which parameters matter for your specific use case.
Bring in consented, human-behavior signals — clicks, dwell, intent, sentiment, and passion — already structured and quality-scored by Wisfer.
Align each behavioral signal to the outcome you care about, so the model learns from data that actually drives your KPI.
Filter by segment, region, and freshness; blend sources; drop noisy signals — shaping a training set tuned to your use case.
Run LoRA, QLoRA, or full-parameter fine-tuning on the customized behavioral data, with sensible defaults per model family.
Score against your baseline across ten dimensions, then promote the winner — or schedule an A/B test to decide in production.
Queue batch re-training on fresh behavioral data so the model keeps improving without manual runs.
Pick your use case — Wisfer scores every behavioral parameter by impact so you can drop what's irrelevant and train leaner.
Roll out a fine-tuned variant against your baseline on a schedule and let live metrics pick the winner.
Queue recurring retraining jobs on fresh behavioral data so performance keeps compounding — hands-off.
Customized behavioral sets remove signals that don't move your metric — less noise, faster convergence.
Every customization is benchmarked, so improvement is proven per metric, not assumed.
Blend and weight signals for your exact vertical, from retail intent to healthcare risk.
Scheduled batches keep the model trained on the latest behavior, avoiding drift.