Real-time policy enforcement platform that sits between employees, their AI agents, and the world — enforcing policies in real time across human and agentic communications. Like spell-check but for policies.
Key Features
Agentic Policy Management - get policies, structure them for AI, add metadata, resolve ambiguity, suggest new policies, Agentic Tenant Context - identify gaps where policies can't be enforced, get context from web or people, Conversational Context Management - track which policies are relevant through conversation turns, Real-Time Policy Enforcement - structured and presented with low latency, InPolicy For Humans - real-time policy enforcement before communication is sent, InPolicy For Agents - policy infrastructure extended to every AI agent, Private Corrections - alerts happen locally, looks like spell-check, Aggregate Analytics - dashboard showing which policies are being triggered and fixed, Inference-time policy injection - policies arrive as structured context object, Any model, any framework - compatible with OpenAI, Anthropic, open-source models via REST API or MCP, Session-aware enforcement - tracks policy activation across conversation turns, Pre- and post-inference checks - context injected before, output validated after, Policy Bot - review contracts, marketing decks, proposals against playbooks
Problem It Solves
AI models don't know company policies or the facts required to enforce them. This is a context problem, not a model problem. AI agents are being deployed at scale but there's no infrastructure to tell them what they're allowed to say.
Target Customer
Legal teams, General Counsel, compliance leaders, marketing teams, anyone responsible for making sure people and agents say the right thing
Use Cases
Client email writing, Regulatory filing summarization, Customer question answering, Document review automation, Preventing harassment & discrimination in communications, Preventing insider trading & quiet period violations, Preventing liability acknowledgments, Sales proposals and marketing deck review, Contract review
Differentiator
Real-time policy enforcement at the moment of communication, like spell-check but for policies. Two products (Humans & Agents) on one policy layer.
Why Now
Agentic AI is being deployed to write client communications, respond to regulatory inquiries, negotiate terms, and execute workflows at scale. But there's no infrastructure to tell AI agents what they're allowed to say.
Traction
Notable Metrics: 80% reduction in first-pass review time