Acumetry makes AI systems safe to deploy across two risk surfaces: what your AI does, and what your AI says. Three products, one thesis — control the actions, ground the answers, and keep both verifiable over time.
Runtime control over what your agents can do — blocking destructive commands, data leaks, and runaway loops before they execute.
Anchors every claim to a real source, builds a citation audit trail, and flags ungrounded output before it reaches a user.
Continuously tests and monitors retrieval quality, answer faithfulness, and data freshness — because grounding isn't set-and-forget.
A policy engine that sits between an AI agent and the actions it can take — shell, files, network, any tool — enforcing your rules before each action runs, plus an audit harness that proves the agent is safe before it ships.
Every tool call is checked against policy first. Dangerous commands, protected paths, and unapproved domains are blocked before execution — with loop, cost, and iteration ceilings built in.
Run your agent through a battery of adversarial scenarios — destructive ops, exfiltration, secret leakage, runaway loops — and get a pass/fail report before go-live.
Security teams control behavior through a readable policy file, not code. Adjust what's allowed, blocked, or flagged — and the audit re-validates instantly.
Ungrounded LLMs invent facts, fabricate citations, quote outdated figures, and burn tokens reasoning toward wrong answers. Grounding anchors responses to real sources — and produces an audit trail proving where every claim came from.
Every claim is anchored to a specific URL, document, or dataset, generating a citation audit trail — so answers are verifiable, not just plausible.
Flags and blocks ungrounded output before it reaches a user — preventing invented facts, fabricated dosages, and outdated financial figures from slipping through.
By grounding answers in retrieved context instead of letting the model reason in circles, you cut wasted tokens and improve accuracy at the same time.
Grounding isn't set-and-forget. Retrieval quality drifts, sources go stale, and index changes silently degrade answers. RAG Evaluation tests your pipeline before launch and keeps watching it in production.
Measures whether the right documents are actually being retrieved for a given query — relevance, recall, and ranking — the foundation everything downstream depends on.
Checks that generated answers are actually supported by the retrieved context — catching the subtle cases where the model strays beyond its sources.
Detects stale sources and monitors quality over time, alerting you when retrieval performance degrades — so you catch decay before your users do.
Start free with the open-source libraries. Upgrade for hosted monitoring, team controls, and audit-ready compliance reporting across guardrails, grounding, and RAG — or bring us in to run a full safety and accuracy audit for you.
Prices shown are launch estimates. Final pricing confirmed during onboarding.
Tell us how your AI is built and deployed — agents, RAG, or both — and we'll point you to the right product or a full audit engagement.