Pylar
Pylar is a secure data access layer designed for AI agents that enables them to safely and efficiently interact with structured data sources through governed SQL views and MCP tools.
https://www.pylar.ai/?ref=producthunt
Product Information
Updated:Dec 5, 2025
What is Pylar
Pylar serves as a critical security and governance layer that sits between AI agents and databases, solving the challenge of giving AI agents safe access to structured data. Rather than allowing direct database access which can lead to security vulnerabilities and compliance issues, Pylar provides a controlled interface where data teams can define exactly what data agents can access through SQL views and Model Context Protocol (MCP) tools. The platform supports connections to major data warehouses like Snowflake, BigQuery, and PostgreSQL, as well as SaaS tools like HubSpot and Salesforce.
Key Features of Pylar
Pylar is a secure data access layer platform that enables AI agents to safely interact with structured data sources. It allows teams to connect multiple databases, create governed SQL views, build MCP (Model Context Protocol) tools, and deploy them to any agent builder while maintaining security and observability. The platform acts as a controlled interface between AI agents and data stacks, providing sandboxed access without direct database credentials.
Governed SQL Views: Create sandboxed SQL views that define exactly what data AI agents can access, with ability to filter sensitive data, implement row-level security, and join across multiple databases
AI-Powered MCP Tool Creation: Generate Model Context Protocol (MCP) tools using natural language or manual configuration to build multiple tools per view that can be published to any agent builder
Multi-Database Integration: Connect to various data sources including warehouses (Snowflake, BigQuery, Redshift), databases (PostgreSQL, MySQL) and SaaS tools (HubSpot, Salesforce) with unified access
Built-in Observability: Track success rates, analyze errors, understand query patterns and use Evals to refine views and tools without redeploying agents
Use Cases of Pylar
Customer Support AI: Enable AI agents to safely access customer data across multiple systems to provide automated support while maintaining data security and governance
Internal Analytics Copilot: Create AI assistants that can analyze company data across databases while ensuring sensitive information remains protected
SaaS Platform Integration: Add AI capabilities to SaaS platforms by allowing controlled access to production data with proper security sandboxing
Sales & Revenue Operations: Build AI tools that can analyze sales data, predict churn, and optimize revenue operations with governed access to sensitive business data
Pros
Strong security and governance with sandboxed data access
Easy integration with multiple data sources and agent builders
No need for complex API development or deployment pipelines
Real-time updates and changes without redeploying agents
Cons
Requires SQL knowledge to create views
Additional layer between agents and data that may impact performance
How to Use Pylar
Sign up and Connect Data Sources: Sign up at pylar.ai and connect your data sources (Snowflake, BigQuery, PostgreSQL, HubSpot, Salesforce etc.) using the connection credentials
Create Governed SQL Views: Use Pylar's SQL IDE to create views that define what data agents can access. Write SQL queries to join across databases, filter sensitive data, and implement row-level security. Views act as the only access layer between agents and raw data.
Build MCP Tools: Create MCP tools from your views either using natural language prompts or manual configuration. Each view can have multiple tools built on top of it. Tools define how agents can interact with the data.
Test and Configure Tools: Test your MCP tools before publishing. Set query limits, frequency caps, and other guardrails. Use the built-in evaluation system to analyze tool performance.
Publish Tools: Publish your MCP tools to get a single MCP server URL and authorization token that can be used to connect the tools to any agent builder.
Connect to Agent Builders: Use your MCP URL and token to connect your tools to agent builders like Claude, OpenAI, Cursor, VS Code, LangGraph etc. Changes to tools in Pylar automatically reflect across all connected builders.
Monitor and Iterate: Track success rates, analyze errors, and understand query patterns using Pylar's Evals system. Refine views and tools based on real usage data without having to redeploy agents.
Pylar FAQs
Pylar is a secure data access layer for AI agents that enables them to interact with structured data sources without requiring direct database access. It sits between AI agents and databases, allowing organizations to define what data agents can access through SQL views while maintaining security and governance.
Pylar Video
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