Transforming Business with AI
Semantic data protection runtime for AI systems. Policy engine, PII detection and reversible tokenization between your application and any LLM.
Production LLM systems share a failure class: PII leaking into prompts and outputs, hallucinated data reaching customers, and generated text violating communication policies. Ad-hoc filters solve one incident and break on the next edge case. GDPR makes this an architecture problem, not a patching problem.
OGuardAI runs as a synchronous filter in the LLM request and response path. Three validation layers: content policy classification against hot-reloadable YAML rule sets, PII detection combining pattern matching with named entity recognition, and semantic tokenization that swaps sensitive values for reversible tokens so the model never sees raw data. Restoration happens per output channel policy.
System Architecture: LLM Response, OGuardAI, Policy Check, Pass, Deliver to User, Violation, Content Classifier, PII Found?, Yes, Redact / Tokenize, No, Block + Reason, Upstream Retry
Guardrails only work if they run before the model sees data and before output reaches a user. OGuardAI sits inline rather than as an after-the-fact audit, accepting a small, bounded latency cost for enforcement that cannot be skipped.
Redaction destroys the context a model needs to answer well. OGuardAI swaps sensitive values for structure-preserving tokens, so the model reasons over coherent text and never sees raw data. Restoration happens per output-channel policy, which makes the token vault the asset to secure.
Communication and data rules change faster than release cycles. Policies are YAML that reloads without a restart, so operations can tighten or relax rules live instead of waiting for a deploy.
Regular expressions catch known formats; named-entity recognition catches contextual PII a pattern misses. Running both raises detection recall instead of betting on one technique.
A reusable, framework-agnostic guardrail layer that turns GDPR-aware AI from per-project firefighting into infrastructure. Proprietary Oronts product; the architecture is documented in our data-leakage guide.
OGuardAI drops into a client's AI stack as the layer between the application and any model provider.