Data Engine

Data Engine is the telemetry and continuous improvement system of the Menlo Stack. It closes the development loop by capturing operational evidence and feeding it back into training pipelines.

Why We Built Data Engine

Real-world data is irreplaceable. No simulation can capture every edge case. The only way to achieve true reliability is to learn from actual deployments.

What Data Engine Provides

Operational Evidence Capture

Data Engine records what failed, where, under what conditions, and with what impact. Every failure becomes training data. This includes:

• Agent behavior logs
• Motor control outcomes
• Sensor readings during operations
• Success/failure classification
• Environmental context

Closed-Loop Improvement

Real-world evidence flows back into Uranus scenarios and Cyclotron training pipelines. Every deployment makes the next one smarter:

• Uranus scenarios are updated with new edge cases
• Cyclotron training incorporates real-world failures
• Agent policies are refined based on operational data

This is the Menlo flywheel: more deployments → more data → better policies → more deployments.

Data Architecture

Collection

Telemetry is streamed from deployed robots in real-time. Collection is automatic—every operation generates data.

Processing

Raw telemetry is processed to extract actionable insights:

• Failure pattern detection
• Skill performance metrics
• Environmental classification
• Training example generation

Storage

Processed data is stored in versioned datasets:

• Historical data for trend analysis
• Curated training sets for Cyclotron
• Scenario libraries for Uranus

Integration

Data Engine connects:

• Agent Platform: Receives telemetry from deployed agents
• Uranus: Provides real-world data for scenario construction
• Cyclotron: Serves training data for motor skill improvement
• Asimov: Captures operational data from physical deployments

Robustness Through Iteration

Robustness is achieved through iteration. Data Engine ensures that iteration is informed by reality—not simulation assumptions, but actual deployment outcomes.