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ControlisAI

ControlisAI gives enterprises pre-call governance, risk blocking and audit-grade visibility for AI/LLM inference, so teams can run and scale AI workloads across dev, staging and production with full control.
Rating:
5
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LLM governanceAI inference control planeenterprise AI risk blockingprompt security & data boundary protectionAI call audit trailmulti-environment AI ops

Features of ControlisAI

Policy check BEFORE the model is invoked—block non-compliant requests instantly
Edge tokenization of sensitive data to keep raw PII from leaving your perimeter
Full audit log with policy version, context metadata and execution trace—export ready
Cross-app, cross-team observability to analyze cost drivers and usage patterns
Track runtime drift and reuse to optimize inference operations
Multi-environment lifecycle management from dev to prod with gated promotions
Zero-agent, zero-middleware, model-agnostic—drop into existing stacks without rewrites

Use Cases of ControlisAI

Pre-flight every LLM call for policy & risk before go-live
Mask customer data in support-chat bots to stay GDPR/CCPA compliant
Export audit trails for regulators or internal risk reviews
Compare per-app spend and spot the cost outliers
Isolate environments for data-science, QA and prod teams under one governance layer
Enforce a single policy engine across regulated product lines
Scale multi-model apps without vendor lock-in while keeping governance consistent

FAQ about ControlisAI

QWhat is ControlisAI?

ControlisAI is a governance and control plane that sits in front of your enterprise LLM inference endpoints, evaluating policy, risk, cost and context before any model is called.

QWhich problems does it solve?

It stops policy violations, data leaks and blind-spots in inference, and gives you an audit-ready decision trail.

QWhat does “pre-call governance” mean?

Rules and risk checks run before the request reaches the model; the system can allow, block or modify the call.

QCan I export audit records?

Yes—policy versions, context metadata and execution traces are logged and can be exported for compliance reviews.

QHow does it handle sensitive data?

Sensitive fields are tokenized at the boundary so raw data never leaves your environment.

QDo I need to rewire my stack?

No agents, no middleware, no model binding—deploy without changing existing traffic paths.

QWho should use it?

AI platform teams, compliance & risk groups, and any engineering org running multi-app, multi-environment LLM services.

QIs pricing public?

Pricing and tier details are not listed; contact the vendor for a custom quote.

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