Prahar
Sanskrit: “Guard / Watch”
Ship models that have already survived their worst day.
Adversarial stress-testing that finds your model's breaking points, then builds the training data to eliminate them — before production.
How it works
Adversarial Prompt Generation
AI generates thousands of prompts designed to expose your model's failure modes — edge cases, ambiguity, domain gaps, adversarial inputs.
Failure Surface Mapping
Every generated prompt is run against your model. Failures are captured with full context: input, output, expected output, failure type.
Corrective Annotation
Failed prompt-response pairs are annotated with correct outputs by domain experts. These become your inoculation training data.
Corrective Fine-Tuning
Annotated failure data is packaged for fine-tuning. Your model learns from its own failures before they reach production.
Pre-Ship Validation
Same adversarial prompts re-run post-training. Before/after comparison proves which failure modes were eliminated.
Simple, transparent pricing
Sprint
One adversarial pass: 1,000 generated prompts, failure mapping, annotated corrective dataset.
Full Inoculation
Three adversarial rounds with escalating difficulty. Includes fine-tuning support and validation report.
Continuous Guard
Monthly adversarial sweeps as your model and data evolve. Prevents regression.
Request a Pilot
No long-term commitment. Results in weeks, not months.