The Semantic Control Layer
for AI
AICP makes every AI decision traceable, reproducible, policy-enforced, and auditable without requiring changes to your existing code.
Join the waitlist to get early access
One line change. Full governance.
from openai import OpenAI
# After — one line change, full governance
from aicp import OpenAI
Why AICP Exists
Your company is deploying LLM-powered features faster than your governance can keep up. You can't answer basic questions like "Which models break if this training dataset changes?" or "Did this AI decision comply with our data policy?"
But only 6% of organizations have advanced AI security strategies.
The governance gap is growing faster than the AI adoption gap.
The Missing Layer
Data catalogs know datasets. MLflow knows models. LangSmith knows prompts. Nothing connects them end-to-end. AICP is the semantic control layer that holds the full context of every AI decision.
What AICP Does
A transparent gateway that sits between your applications and their LLM providers—giving you complete audit trails, end-to-end lineage, and real-time policy enforcement.
Immutable Decision Records
Every AI decision is captured as a structured, immutable, context-rich artifact: what model was used, what prompt template, what inputs went in, what came out, how much it cost, how long it took.
- Tamper-proof decision records
- Time-series storage for historical analysis
- Point-in-time temporal queries
Metadata Knowledge Graph
A knowledge graph that connects datasets, features, models, prompts, decisions, and business outcomes. Ask "what breaks if I deprecate this model?" and get instant answers.
- Dataset → Feature → Model → Decision lineage
- Impact analysis via graph traversal
- Blast radius queries
Runtime Governance
Define governance rules with conditions and actions. Block requests that exceed cost thresholds. Detect and flag PII before it reaches an LLM provider. Policies evaluate at runtime, before execution.
- Policy-as-code for version control
- Automated PII detection and redaction
- Human-in-the-loop approvals
How AICP Works
AICP coordinates three execution planes—data, model, and agent—through a unified control layer that provides metadata graphs, policy enforcement, and audit trails.
Control Plane Coordination
Agentic Orchestration Flow
See AICP in Action
Explore the metadata graph, enforce policies, and trace every decision
End-to-End Lineage
Trace from datasets to business outcomes
Policy Enforcement
Define and enforce governance rules
Metadata Graph Details
Inspect node metadata, relationships, and policy tags
Built for Regulated Industries
Deploy AI agents with confidence in FinTech, HealthTech, and LegalTech where compliance and reliability are non-negotiable.
Financial Services
Ensure every trading decision, loan approval, and customer interaction is traceable, compliant, and explainable.
Healthcare & Life Sciences
Protect patient data with real-time PII detection while enabling AI-powered diagnostics and care coordination.
Enterprise SaaS
Scale AI features without scaling your debugging team. Reduce diagnostic time from days to minutes.
Who This Is For
Platform teams, ML engineers, data engineers, compliance officers, and engineering leaders who need to make AI governance a byproduct of operation, not an afterthought.
Change from openai import OpenAI to from aicp import OpenAI or set your LLM API base URL. No migration project.
Built for AI Agents Who Execute
Agents need semantic context to make safe, auditable decisions. AICP provides the operational intelligence that transforms agents from blind executors into context-aware orchestrators.
Make compliance a byproduct of operation
Audit prep shrinks from weeks to a single API call, because the evidence trail was created as decisions were made, not reconstructed afterward.