Structured context for data workflows

A programmable decision layer for your data. Instructions, policies, and context as metadata for automation and agent-based systems.

retention:7ypii.sensitiveaudit:required
pii.maskedgdpr.consentanonymize:90d
gdpr.art6freshness:15mml.approved
weight:0.95inherits:orders.amountstale_after:5m
no_exportsync.prohibitedaccess:09-17_ET

Decision-grade data instructions

Metatate keeps business logic, policies, and access rules as structured, machine-readable instructions for platforms, agents, and workflows.

Persistent Context

Business logic, data contracts, policies, lineage, and access rules as structured, machine-readable context.

Programmable Rules

Agents evaluate dynamic business rules instead of relying on hardcoded branches or human interpretation.

Versioned & Portable

Context layers are versioned, allowing policies to evolve over time.

MCP Server
Processing queries with instructions
Active instructions:8
Decisions/min:~24
Instructions Library
PII protection rules
90-day retention policy
Access tier requirements
Audit trail requirements
Service tier checks
Live and queryable
Live Decision Stream

Programmable decision layer

Policy-as-code

Instructions and policies are versioned, reviewable operational metadata. Plan/apply workflow like infrastructure-as-code.

Version controlled policies

Track every change with Git-style versioning and rollback capabilities.

Plan before apply

Preview changes before deployment with impact analysis.

Collaborative review

Pull request workflow for governance changes with team approval.

policy-workflow
1
metatate plan

Preview changes

+ Add policy
~ Update scope
→ 2 tables
2
Review

Team validation

Steward approved
Security reviewed
3
metatate apply

Deploy to production

✓ Policy deployed (v2.1.0) • ✓ 2 tables updated • ✓ Audit log created

How it works

Author and deploy data policies, enrich tables with business meaning, validate intended uses, and expose that context for agent workflows.

Policy Identity

Identity

v2.1.0
Name:customer-data-ai-access-policy
Status:
Active
Created:2024-08-15
Last Modified:2025-03-10
Description:Policy restricting AI model training on customer data, allowing agentic use with PII encryption/anonymization
Scope:All customer PII datasets
Tags:
ai-governancepiicustomer-data
Owner:
Data Governance
Domain:
Customer

Data That Carries Its Own Instructions

Persistent context means consistent decisions

Context that stays with your data ensures agents and systems make the same decisions across analytics, AI, and operational workflows.

Without persistent context

Context stored in separate systems

Passive documentation

Static tags—agents infer intent

Rigid implementations that break with updates

With persistent context

Structured, machine-readable context attached to data

Operational, decision-grade instructions

Programmable logic—agents query and execute

Deploy once, version and evolve over time

Flexible deployment

Native warehouse integrations or cross-platform agent coordination. Metatate adapts to how you work.

Native Controls

Deep native integrations means governance built into data development.

Warehouse
Discover data
allow
Decide data
deny
Inspect data
allow
Authorize data
modify
Validate data
allow
Explain data
deny

Multi-agent

Multiple agents query the shared decision layer before accessing data. Structured context becomes the coordination protocol between agents.

MCP Server
MCP Server
Sales Agent
modify
Analytics Agent
deny
MCP Server
Support Agent
allow
Finance Agent
deny
MCP Server
MCP Server
Marketing Agent
allow
MCP Server

Built for Automation

Context that moves with your data. Agents and systems can coordinate, enforce policies, and validate compliance without manual oversight.

01

Agents understand meaning, not just structure

Agents combine data from multiple sources understanding calculations, relationships, and constraints without human translation.

02

Scale domain expertise to every decision

Systems apply complex business logic consistently across workflows.

03

Policy enforcement without bottlenecks

Governance happens automatically because agents understand constraints.