Single Origin powers AI agents: query optimization, query code review, query debugging, with deep data warehouse context. We provide a turnkey Model Context Protocol (MCP) server that transforms your enterprise's raw query history into a global context graph.

The Challenge: Agents Lack Deep Data Context

Enterprises are rapidly adopting Agentic AI (like Cursor, Claude Code, or custom frameworks) to accelerate data engineering and manage infrastructure. However, these agents run into a massive roadblock: they lack the specific, historical context of your data warehouse.

Generic LLMs only understand public SQL syntax. They don't know your company's private business logic, how your tables relate to one another, or where your execution bottlenecks live.

When engineering teams try to build this deep context themselves, they hit major hurdles:

The Cost of Context: Brute-forcing petabytes of raw query logs through standard RAG pipelines is prohibitively expensive and noisy. (For example, LinkedIn spent ~$3M on a single LLM run just to analyze one week of query logs).

Closed Ecosystems: Major compute providers (Snowflake, Databricks, AWS) hide their internal execution logic, making it nearly impossible for internal teams to systematically generate a true context graph.

Missed Insights: Without a structured way to analyze historical compute patterns, agents cannot uncover the hidden optimization opportunities buried in your data infrastructure.

The Solution: The Enterprise Context Graph

Single Origin acts as the intelligence layer between your data warehouse and your AI agents.

Instead of spending millions trying to parse raw logs with LLMs, Single Origin uses proprietary clustering algorithms to process your entire production query history, schemas, and metadata. We distill this fragmented data into a deep context graph—and expose it directly to your agents via our MCP server.

This gives your AI workflows the precise intelligence they need to operate efficiently from Day 1.

Why Single Origin? (Our Technical Moat)

• Uncover Hidden Insights: Your private query history is full of untapped optimization opportunities. Single Origin's context graph reveals identical query clusters, unused columns, and execution bottlenecks, allowing agents to upgrade your infrastructure efficiently.
• Unmatched Token Efficiency: We bypass the massive token costs of traditional context-gathering. Our specialized clustering algorithms deliver highly condensed, deterministic evidence directly to the agent’s context window.
• Ready on Day One: Stop trying to build your own context pipelines. We provide native dialect parsers for the major data warehouses (Snowflake, BigQuery, Databricks, AWS), delivering out-of-the-box context for your agents.

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.