The problems this team brings to us.
Built for enterprise AI.
How it works for your team, day to day.
Guardrails as middleware, not a rewrite
The SDK drops into your backend as Python, Node, or Go middleware. Most teams are enforcing policy on production traffic in under 24 hours. Input policies screen what users send in; output policies screen what your feature says back. Your application code doesn't change.
Abuse and cost under control
Per-user and per-tier rate limits, token budgets with hard caps, and anomaly detection for scripted abuse. When a user trips a limit, the response is a clean product decision (throttle, downgrade, or suspend) instead of a surprise on the invoice.
Ship new models without holding your breath
Model changes roll out behind policy gates: route a fraction of traffic, watch the guardrail telemetry, and roll back in seconds if behavior drifts. Prompt-injection defense and PII redaction apply uniformly across whichever model serves the request.
The questions this team asks first.
How much latency does the SDK add?
Inline evaluation runs at sub-38ms p99, below perceptual thresholds for chat-style features and far below typical model inference time.
Do you support self-hosted models?
Yes. The middleware sits between your application and any inference endpoint, hosted APIs and self-hosted models get the same policies.
How is our users' data handled?
Policy evaluation happens inline; prompt content is not used to train models. Retention of logs is configurable, and PII can be redacted before it ever reaches your model or ours.
Can policies differ by product tier?
Yes. Policies are scoped, so free-tier users can carry tighter budgets and stricter content rules than enterprise seats, from one policy file.