Control what your AI tools send to models.
An LLM gateway between your people, your agents and every model provider. Route, redact, cap and record every call, and stop prompt injection and data leaks before they reach a model.
Point your tools at the gateway. The policies do the rest.
- 01
Point
Swap the base URL in your SDKs, IDEs and agents. Provider keys stay in the gateway and never sit on laptops.
- 02
Decide
Which models each team may use, which tools an agent may call, how much a team may spend.
- 03
Inspect
Every prompt and response is checked for secrets, personal data and injection attempts, in both directions.
- 04
Record
An audit trail of every call: user, agent, model, tokens, cost and the verdict.
What the gateway checks before a prompt reaches a model.
Identity, data, injection, tools and budget. A prompt that passes all five goes through; the rest are masked, held or stopped.
LLM firewall
Prompt injection, jailbreaks and data exfiltration patterns are caught on the way in and on the way out.
Redaction, not refusal
Card numbers, keys and personal data are masked before the prompt leaves, so work goes on.
Tool-call policies
Agents may read the wiki and open tickets, but not push to main or run a shell, unless you say so.
Model allow-list
Approved providers and models per team, with self-hosted models for the sensitive work.
Spend you can see
Budgets and rate limits per team, agent and key, with an alert before the bill.
Evidence for AI rules
Usage, policies and incidents mapped to the NIST AI Risk Management Framework.
Built for what you run.
Routes to
- Anthropic
- OpenAI
- Gemini
- Mistral
- Ollama
- AWS
- Azure
- Any OpenAI-compatible endpoint
Part of one platform.
AI agent governance is in development. This page shows where it is going; details will change as we build it with early-access customers. Today's product is on the platform pages.
Want it first? Build it with us.
Early-access customers shape what ships: tell us what you run and what you need it to do.