Close isn’t compliant.
LLMs can read your documents. They can summarize risk and sound confident doing it. But they weren’t built to decide what truly matters. HarmCheck was. HarmCheck catches risk the moment it happens, with every flag traceable, auditable, and tied to real liability.
HarmCheck vs.
Generative AI
LLMs look strong at a glance. When results need to be auditable, defensible, and compliance-ready, the gaps start to appear.
| Generative AI (LLMs) | HarmCheck | |
|---|---|---|
| Role | Explore and summarize | Detect and prove risk |
| Granularity | Themes and groupings | Exact sentences |
| Precision | Approximate | Exact |
| Output | Narrative text | Structured, labeled |
| Consistency | Changes run-to-run | Repeatable |
| False positives | High | Low |
| Hallucinations | Common | Impossible by design |
| Auditability | Not defensible | Audit-ready |
| Best use | High-level understanding | Compliance decisions |
HarmCheck was built for accountability.
Optimized for language, not compliance
- High false positives
- Approximate, theme-based detection
- Unstructured, narrative output
Purpose-built for compliance
- Exact, sentence-level detection
- Structured, labeled output
- Consistent, audit-ready results
AI without surrendering control.
LLMs often require sending sensitive data externally with variable costs, but HarmCheck is built for real environments:
Controlled deployments
Deploy on-prem or in your own controlled environment — your data never leaves your perimeter.
No black box outputs
Every flag is structured, traceable, and auditable, so your team always knows why a result fired.
Predictable cost at scale
Stable, transparent pricing with no per-token surprises as your volume grows.
See it. Stop it. Prove it.
Identify risk with precision and deliver results that hold up under scrutiny.