Single Origin powers AI agents with your enterprise's complete, private query history via a Model Context Protocol (MCP) server, turning generic LLMs into infrastructure-aware experts that optimize data with absolute precision.

Instead of guessing based on public SQL syntax, agents connected to Single Origin gain "Enterprise Memory"—deterministic, historically grounded evidence of exactly how your business queries its data.

The Problem: AI Agents Lack Enterprise Context

Enterprises want to use Agentic AI to automate data engineering, but generic LLMs only know public syntax. To work safely, agents need deep, historical context of how your business actually queries its data.

When teams try to build this context themselves, they hit three major roadblocks:

Prohibitive Cost: Brute-forcing petabytes of query logs through LLMs is too expensive and noisy. (For context, LinkedIn spent ~$3M on a single LLM run just to analyze one week of logs).

Zero Margin for Error: Actions like deleting tables or altering columns require exact row- and column-level precision that generic RAG pipelines cannot guarantee.

Closed Ecosystems: Major compute providers (Snowflake, Databricks, AWS) hide their internal execution logic, making DIY context graphs nearly impossible to build.

The Solution: Enterprise Memory via MCP

Single Origin solves the context bottleneck by providing a turnkey, mathematically efficient MCP server that bridges the gap between your massive, messy query logs and your AI agents.

We ingest your entire production query history across any compute platform and distill it into a highly efficient context graph. We then expose this intelligence directly to your AI agents via standard MCP endpoints.

Why Single Origin? (Our Technical Moat)

• Unmatched Cost Efficiency via Proprietary Clustering: We don't rely on expensive, noisy LLM inferences over raw logs. Our specialized clustering algorithms (list_similar_query_groups) map your context graph with extreme efficiency, saving millions in compute and token costs.
• Absolute Precision: Single Origin doesn't guess. Because we map exact table, column, and row-level usage, our MCP gives AI agents the deterministic evidence required to execute structural changes with zero margin for error.
• Native Parsers for Closed Systems: We’ve done the heavy lifting of reverse-engineering and parsing the execution patterns across closed-source platforms so your internal team doesn't have to spend years building parsers.

How It Works

Single Origin integrates seamlessly into your agentic workflow, acting as the intelligence layer between your data warehouse and your AI.

1. Connect: Link Single Origin to your compute platforms (Snowflake, BigQuery, Databricks, AWS, Trino, etc.).
2. Compile: Our proprietary parsers ingest your execution history, metadata, and lineage to build a rich, mathematically efficient context graph.
3. Expose: You connect your AI agents to the Single Origin MCP server.
4. Execute: Agents query our MCP tools in real-time to gain historical evidence, enabling them to confidently write PRs, optimize slow queries, or safely deprecate unused assets.

Core MCP Capabilities

By connecting to Single Origin, your AI agents immediately gain access to powerful tools, including:

table_detail: Retrieve deterministic table schemas, historical usage stats, and column-level lineage to understand dependencies before making changes.

query_profile_detail: Feed exact historical execution bottlenecks directly into the LLM context window to automate performance tuning.

list_similar_query_groups: Instantly cluster millions of historical queries to find the exact precedent an agent needs to write safe, company-compliant code.

Trusted by Data-Forward Teams

We are the standard for infrastructure teams scaling Agentic AI.

• Roblox: Reduced metric storage by 15% and eliminated billions of unneeded time series.
• Coinbase: Ensures precise data context for their engineering workflows.
• Palo Alto Networks: Secures their data foundation for rapid AI adoption.

Our team consists of senior data infrastructure leaders from Uber, Snap, Stripe, and Meta. We built the internal tools that scaled these data platforms, and now we are bringing that exact capability to the Agentic Economy.