AI adoption is happening inside your organization right now. Anie sits between your teams and the AI they use — enforcing policy, managing credentials, logging every action, and giving IT full control without slowing anyone down.
AI adoption is happening inside your organization right now — in pockets, in silos, in ways nobody planned for. Teams are wiring agents to production CRMs, internal databases, email inboxes, and financial systems. They're sharing credentials over Slack, using personal API keys, and building workflows nobody approved or audited. IT and security are caught in an impossible position: block AI and slow the business, or allow it and watch risk accumulate with no safety net.
Nobody knows what agents are running, what tools they're connected to, or what data they're accessing. There is no single pane of glass. You can't govern what you can't see.
Teams connect agents to production systems using personal API keys, shared service accounts, and ad-hoc OAuth tokens. No centralized credential management, no scoped access, no revocation.
When something goes wrong, there is no way to reconstruct what happened. No log of what the agent was asked, what it decided, what tools it called, or who approved it.
Every team picks their own tools, their own models, their own integrations. Six different agents from six different vendors, each with its own access patterns. No consistency, no scale.
Who approves what? Which actions need human sign-off? Without structured approval workflows, teams either skip approvals entirely or get stuck in untrackable back-channel requests.
The real risk isn't the AI you can see — it's the AI you can't. Agents built on personal accounts, copilots with ungoverned tool access, third-party services touching your data without your knowledge.
Every agent action flows through a governed pipeline — identity, policy, tool access, execution, and audit — before a response reaches the user.
A user sends a message through Slack, Teams, email, or the web. The channel adapter normalizes the message and resolves the user's identity through a four-level cascade: organization → department → team → individual. The right persona, tone, guardrails, and topic boundaries are injected automatically.
Before any action is taken, the policy engine runs. Hard guardrails are enforced pre-flight: PII detection and redaction, data access scope verification, approval gates for sensitive actions, model restrictions, and rate limits. Soft guardrails are evaluated post-hoc and flagged for review.
The agent calls only the tools it's authorized to access through scoped OAuth connectors with per-tool, per-user credentials. Every tool call is logged. Honeypot tools catch prompt injection attempts in real time. Unauthorized access triggers immediate security alerts.
The response is checked against policy before delivery — redacted if it contains PII, escalated if it needs human approval, blocked if it violates policy. Every action is recorded in an immutable, tamper-evident audit log. Full replay. Full traceability. No gaps.
Policy enforcement, audit, identity, and intrusion detection — built for enterprise from day one.
Hard guardrails enforced pre-flight, before any action reaches a tool.
Immutable, tamper-evident ledger for every agent action — end to end.
Intrusion detection built for AI agents — catches what perimeter tools miss.
Structured approval workflows so the right actions always get human sign-off.
"I need to scale AI without scaling risk."
"Show me what an auditor would see."
"I need to know what we're spending on AI and what it's touching."
Govern what agents can touch. Trust what they do. See your enterprise AI adoption done right — with visibility, control, and audit built into every action.