Power AI Agents with Deep Context from Your Data Warehouse

Turn raw query logs and operational data into a deep context graph to unlock the hidden insights your AI agents need to streamline workflows and optimize your data warehouse usage

AI Agents Are Blind to Your Production Reality

Enterprises are rapidly adopting Agentic AI to accelerate data engineering. But generic LLMs only know public syntax—they don't understand your private business logic, historical execution profiles, or specific data constraints.

The Cost Trap

Feeding raw, petabyte-scale query logs into LLMs to build context is prohibitively expensive and extremely noisy.

The Precision Bottleneck

Deleting storage or altering tables requires a zero margin for error. Generic RAG pipelines simply aren't precise enough at the row, column, and table level.

Closed Ecosystems

Major compute engines (Snowflake, Databricks, AWS) hide their execution logic, making it nearly impossible to build your own context graphs.

Give Your AI Agents “Enterprise Memory”

Single Origin bridges the gap between your massive, messy query logs and your AI agents. We provide a turnkey MCP server that continuously builds a highly efficient context graph from your actual production compute history.

For Code Workflows

Streamline the journey from insight to merged PR with historically accurate evidence.

For Infrastructure

Safely prune unused storage and optimize pipelines with deterministic confidence.

Built for Scale - Unmatched Context Efficiency

Why not just build a RAG pipeline over your query logs? Because brute-forcing context is unscalable.

Proprietary Clustering

Instead of spending millions of dollars on LLM tokens to parse noisy, petabyte-scale query logs, our specialized clustering algorithms extract perfect context at a fraction of the compute cost.

Absolute Precision

Our tools don't guess. We map exact table, column, and row-level usage so agents can execute structural changes with zero margin for error.

Native Dialect Parsers

We've reverse-engineered and parsed the execution patterns across closed-source platforms (AWS, GCP, Databricks, Snowflake) so your internal team doesn't have to.

Plug Directly Into Agentic Workflow

Works natively with Claude, Cursor, Codex, and any MCP-compatible client — no new UI to learn, no workflow to change.

Optimization Recommendations

Surface actionable cost and performance improvements backed by real execution history.

Query Profiling

Feed exact historical execution bottlenecks directly into the LLM.

Cluster Analysis

Retrieve groups of similar historical queries for deep cluster-wide analysis.

Built by Data Infrastructure Veterans

Our team has built and scaled data infrastructure at companies processing petabytes of data daily. We're bringing that expertise to redefine how modern data teams work.

Stop guessing. Start optimizing with precision.