Pylar

Product Introduction

1. Pylar is a secure data access platform that acts as an intermediary layer between AI agents and organizational data sources. It enables technical teams to define precise data visibility rules through governed SQL views, which are then compiled into reusable MCP (Managed Compute Platform) tools. These tools can be securely published to any AI agent framework while providing comprehensive observability across all deployments.
2. The core value lies in its ability to enforce granular data governance while accelerating AI agent deployment. By abstracting raw database access through customizable views, Pylar eliminates unauthorized data exposure risks and reduces development cycles from weeks to minutes. Organizations maintain full control over row-level security, query patterns, and sensitive field masking without compromising agent functionality.

Main Features

1. Governed Data Views allow administrators to create SQL-based data sandboxes with precise access controls. These views support cross-database joins (e.g., combining Snowflake analytics with HubSpot CRM data), implement row-level security filters, and automatically mask sensitive columns. Agents interact exclusively through these pre-vetted interfaces, preventing raw table access while enabling complex unified queries across disparate sources.
2. MCP Tools Generation transforms SQL views into reusable API-like endpoints through natural language prompts or manual configuration. Users can create multiple tools per view (e.g., fetch_customer_health and get_subscription_status from a single customer view), test them in an integrated SQL editor, and validate query patterns before deployment. Each tool includes parameter validation and automatic schema documentation.
3. Universal Publishing provides one-click deployment to agent frameworks via secure HTTPS endpoints with OAuth tokens. Published MCP tools automatically synchronize across all connected platforms (LangChain, Claude, Zapier, etc.), with changes propagating instantly without redeployment. Real-time monitoring tracks usage metrics, error rates, and cost-per-query across every integrated environment.

Problems Solved

1. Pylar eliminates uncontrolled AI agent access to sensitive databases, preventing accidental PII leaks and schema exposure. Traditional agent deployments require custom API development with fragile authentication layers, whereas Pylar enforces view-based governance that dynamically filters unauthorized data at query runtime. This reduces security review cycles and prevents costly data breaches from misconfigured agents.
2. The platform targets data engineers, AI developers, and security teams building production-grade AI agents. Technical leaders responsible for maintaining compliance (like Heads of Engineering or RevOps) benefit from its audit capabilities, while data teams accelerate tooling creation without backend development.
3. Typical scenarios include customer support agents accessing filtered ticket histories, sales bots retrieving segmented CRM data, and analytics agents generating revenue forecasts from governed financial datasets. Enterprises use it to deploy secure customer-facing AI interfaces on SaaS platforms while maintaining strict data isolation.

Unique Advantages

1. Unlike basic API wrappers, Pylar implements true zero-trust architecture with credential isolation through cloud KMS and query abstraction. Competing solutions like direct database connectors lack row-level security, while custom-built middleware requires ongoing maintenance for schema changes. Pylar uniquely combines SQL flexibility with agent-specific guardrails.
2. Key innovations include natural-language MCP tool generation (converting prompts like "Create a tool to fetch customer health by email" into production-ready endpoints) and live cross-database joins. The platform's fingerprinting system analyzes query patterns to detect anomalies, while Evals automatically track cost-per-call and success rates across deployments.
3. Competitive advantages include framework-agnostic publishing (single configuration works for 10+ agent builders) and bidirectional synchronization. When views are updated (e.g., schema modifications), all connected agents immediately inherit changes without manual intervention. This reduces deployment time from weeks to hours while providing unmatched observability.

Frequently Asked Questions (FAQ)

1. How does Pylar prevent unauthorized data access? Pylar isolates credentials using cloud KMS and restricts agents to pre-approved SQL views with embedded security rules. All queries execute through parameterized MCP tools that enforce input validation, while raw database connections remain completely inaccessible to agents. Real-time violation tracking blocks suspicious patterns.
2. Can Pylar combine data from multiple sources? Yes, the SQL editor enables joins across any connected database (e.g., unifying Postgres billing data with Snowflake usage metrics). Views compile these federated queries into single endpoints, allowing agents to retrieve combined datasets without custom integration code or data duplication.
3. How are updates handled in production environments? Modified views automatically propagate to all published MCP tools, with changes instantly available to connected agents via Pylar's streaming endpoint. The platform maintains version compatibility during updates and provides rollback capabilities through the Evals dashboard.