Drop-in proxy that sits between your applications and every LLM provider. HIPAA PHI redaction, prompt injection blocking, cost optimization, and full agentic governance — zero code changes required.
Every AI request passes through four governance layers before reaching any LLM.
Built for enterprises that need governance, not just a proxy.
15 entity types including MRN, NPI, ICD-10, CPT codes, drug names, and provider identities. Complete round-trip: redact before LLM, restore in response. Zero PHI exposure.
35+ patterns covering jailbreaks, token smuggling, indirect injection, DAN attacks, role reassignment, and system prompt extraction attempts.
OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Gemini, Groq, Mistral, DeepSeek, Cohere, Fireworks, Together, Ollama, and more. Switch providers with zero code changes.
Content-aware cache with LRU eviction and configurable TTL. Semantically similar queries hit cache — 40%+ cost reduction on repetitive workloads.
Daily and monthly spend limits per user and team. Real-time budget enforcement with configurable alert thresholds before limits are hit.
Sub-agent depth limiting, per-run cost caps, infinite loop detection via fingerprinting, and consistent PHI redaction across all 80 turns of an agent session.
Prometheus metrics, OpenTelemetry traces, structured audit logs, anomaly alerting via PagerDuty/Slack/webhook. Every request, every decision, immutable.
Round-robin, weighted, least-latency, cost-optimized, quality-scored, and fallback. Circuit breaker per provider. Data residency enforcement for PHI traffic.
Whitespace trimming, deduplication, conversation history trimming, and filler removal. Reduce token costs by 15-30% without losing semantic content.
Designed to run inside your perimeter. Your data never leaves your infrastructure.
HIPAA-compliant AI workflows with PHI redaction across all 15 entity types. Clinical output scanning prevents hallucinated diagnoses and dosages. Immutable audit trail for compliance.
PII redaction for customer data, budget controls per team and product line, policy engine to block regulated content, and full audit trail for SOC 2 and regulatory compliance.
Prevent confidential client data from reaching public LLM APIs. Route sensitive matters to on-premise models while using cloud LLMs for non-sensitive work.
Cost controls that prevent budget overruns from students or researchers. A/B testing to compare model quality. Semantic caching to reduce API costs on repetitive queries.
Watch a request flow through all 13 pipeline stages in real time. PHI gets redacted at Stage 6, restored in the response. The LLM never sees real patient data.
Launch Interactive Demo →Start free with our open-source edition. Scale to enterprise when you're ready.