Pylar

Pylar is the secure data access layer that sits between your AI agents and your internal data stack. It eliminates the security risks and engineering overhead associated with connecting large language models (LLMs) to sensitive data sources, enabling you to deploy production-ready agents with guaranteed governance and full observability. If you need to leverage enterprise data from warehouses, databases, or SaaS tools within your AI deployments safely and efficiently, Pylar is your essential control plane.

Key Features

Pylar empowers engineering and data teams to ship AI agents faster and safer by focusing on control, unification, and speed.

• 🔒 View-Level Data Governance: Agents never interact with raw tables or databases. You define exactly what data is accessible by creating governed SQL views, allowing you to filter sensitive data, implement row-level security, and join information across disparate sources. Pylar ensures complete isolation, abstracting credentials and preventing arbitrary queries.
• 🤖 AI-Powered MCP Tool Creation: Dramatically accelerate development by leveraging Pylar’s AI to automatically generate Model Context Protocol (MCP) tools from your defined SQL views. Use natural language prompts like "Create a tool to fetch customer health by email," and Pylar configures the necessary function names, parameters, and underlying SQL, eliminating weeks of manual API endpoint development.
• 🌐 Cross-Database Unification: Unify context for your agents by joining data across multiple sources—including BigQuery, Snowflake, PostgreSQL, HubSpot, and Stripe—within a single governed view. This capability allows agents to access a holistic customer or operational picture without requiring you to build complex custom data integrations.
• 🚀 One Control Pane & Universal Deployment: Publish your MCP tools once and connect them to any agent builder using a single secure URL and token. Pylar is framework-agnostic, supporting LangGraph, Claude Desktop, OpenAI Platform, Zapier, n8n, and more. Crucially, updates to views or tools reflect automatically across all connected builders, eliminating the need for constant agent redeployment.
• 📊 Built-in Observability (Evals): Gain deep, actionable insights into how your agents interact with your data. The Evals dashboard tracks success rates, p95 latency, cost-per-call, and detailed error patterns (e.g., auth_error, schema_error). Use this data to continuously refine your governed views and tools without touching the agent code.

Use Cases

Pylar is designed for teams building mission-critical AI applications that rely on real-time, accurate internal data.

1. Enabling Secure Customer Support Automation

A Head of Data needs to deploy a triage bot that can answer complex customer questions using data stored across Snowflake (usage metrics), Zendesk (ticket history), and Postgres (billing details). Instead of granting the bot raw access or building three separate microservices, the team uses Pylar to create one unified customer_360 view. They then generate specific MCP tools (get_subscription_status, analyze_customer_issues) from that view. The agent receives the unified context it needs, while security remains confident because the agent can only execute the predefined, safe queries within the view.

2. Accelerating Financial Reporting and Forecasting

A Finance Operations team requires an agent to generate weekly revenue forecasts by querying transactional data. The source data is split between a BigQuery warehouse (historical sales) and a Redshift cluster (current inventory). Pylar allows the team to create a single revenue_forecast view that joins these disparate sources. The agent can now execute complex financial analysis tools built on this view, providing precise answers quickly, without the platform team spending weeks building dedicated, secure data APIs for the LLM.

3. Maintaining Compliance in Product Analytics Agents

A CTO is deploying an internal agent to help product managers analyze feature adoption and monthly usage patterns. Since the usage data contains PII, direct access is prohibited. Using Pylar, the team creates a product_usage view that automatically filters out PII columns and only includes anonymized or aggregated metrics. The resulting agent tools, when deployed to development environments like Cursor or VS Code, adhere strictly to the governance policies defined in the view, minimizing the risk of accidental data exposure.

Pylar is specifically engineered to bridge the gap between enterprise security requirements and the dynamic needs of AI agent development.

Conclusion

Pylar provides the essential foundation for scaling AI agent deployments responsibly. By centralizing security, unifying context across fragmented data stacks, and simplifying the tool creation process, Pylar enables your team to deploy powerful, data-aware agents quickly and confidently.

Explore how Pylar can help you achieve production-readiness while maintaining strict data governance.