The simplest, safest way to connect agents to your data stack

Pylar sits between your agents and your databases. You define what data they can access, build custom tools on top of it, and get full observability across all your AI deployments.

Data sources

Agents

Trusted by teams shipping production AI

Quotes

Sarah Li, Head of Engineering
"Security wouldn't let us hook agents straight into Snowflake. Can't say I blame them honestly. Pylar fixed it though. We just tell it what's safe to share, agents get what they need, and our compute costs stay predictable."

Michael Chen, Head of Data
"We've got Postgres and Snowflake connected, merged our customer info from both, and suddenly our n8n and Langchain agents are doing real work. Knocked out 5 tools in one afternoon. Zero API code."

Elena Marquez, Head of AI Platform
"Used to be weeks of work. APIs, endpoints, all that auth stuff. Now? Write one SQL view, Pylar spits out the tools, hook it into Cursor. Takes maybe 10 minutes."

Josh L, Head of RevOps
"Pylar's basically our control center now. Tweak a view? Agents pick it up right away. Messed up a column? Fix it once, everyone sees the update. No more redeploying anything."

David Kim, CTO
"We wanted to put an AI agent on top of our SaaS platform for customers. Security was the big worry. Pylar lets us sandbox everything and set exactly how the AI can touch our data. Went live pretty fast."

Priya Patel, VP of Product
"Forty eight hours from zero to production. That's it. Our agents are answering customer questions using real info. Pylar did the heavy lifting on views, tools, everything. Pretty wild."

How Pylar Works

Connect to your sources, sandbox the data you want exposed, compile it into agent-ready tools, and publish to any agent builder with one secure link.

SQL Views

Views are the only access level. Agents query through your SQL views, never raw tables. Filter sensitive data, implement row-level security, join across databases.

SQL Editor Example

CREATE VIEW customer_analytics AS
SELECT
 c.customer_id,
 c.email,
 c.company_name,
 t.ticket_count,
 t.avg_resolution_time,
 u.monthly_usage,
 u.feature_adoption
FROM customers c
LEFT JOIN tickets t ON c.customer_id = t.customer_id
LEFT JOIN usage_metrics u ON c.customer_id = u.customer_id
WHERE c.status = 'active'
 AND u.last_activity > '2024-01-01';

Evals

Track success rates, analyze errors, understand query patterns. Use Evals to refine views and tools without redeploying agents.

Making AI Agents Production-Ready

Uncontrolled Agent Access

Everyday tools like Claude, Cursor, and ChatGPT can interact with live data without oversight.

How Pylar keeps your data safe

• Credential Isolation: Credentials stored securely using cloud KMS. Agents never see secrets.
• View-Level Governance: Agents query SQL views you define—never raw tables. Full control over rows, columns, and PII.
• Safe Query Abstraction: MCP tools execute predefined SQL. No arbitrary queries, no overexposed data.
• Zero Raw Database Access: Agents never interact with your warehouse directly. Pylar becomes the safe layer in-between.

Datasources

Unify your data stack. Connect warehouses, databases, and SaaS tools—then join them in a single query. Agents get unified context without you building custom integrations.

• BigQuery: Enterprise data warehouse for fast analytics.
• Redshift: AWS cloud data warehouse optimized for scale.
• Snowflake: Cloud-native data platform for unified analytics.
• MotherDuck: Serverless DuckDB-based analytics for small teams.
• Supabase: Open-source Postgres backend for modern apps.
• MySQL: The world's most popular open-source database.
• PostgreSQL: Advanced open-source relational database system.
• MS SQL Server: Microsoft's trusted relational data platform.

Agent Builders

Framework-agnostic by design. One MCP server URL works with LangGraph, Claude Desktop, Zapier, n8n, and every agent builder. Your governance policies travel with the data, regardless of which framework your teams choose.

Quick start

From data view to production agent tool in under 2 minutes. No backend engineering, no API endpoints, no deployment pipeline. Just SQL and natural language.