// lab zone
Deep-tech agent playground.

Test neurosymbolic AI agents on real-world deep-tech problems. Every output is verified by the symbolic layer -- no hallucinations, no unverified claims. See the verification pipeline in action.

3
Agent Labs
100%
Verified Output
0
Hallucinations
~5s
Avg. Latency
// agents
Select a lab to run.

Each lab demonstrates a different deep-tech domain. The neural layer generates candidate output, and the symbolic layer verifies it against formal constraints before displaying results.

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VLSI Design Verification

Semiconductor

Neurosymbolic agent for RTL verification, timing closure analysis, and DRC rule checking. Connects neural pattern matching with formal equivalence checking.

capabilities

  • +RTL code analysis and lint checking
  • +Timing constraint extraction and verification
  • +Design rule violation detection
  • +Formal equivalence proof generation
  • +Power estimation and optimization hints
[ ]

Sensor Fusion Pipeline

Edge AI

Multi-modal sensor data fusion agent with symbolic constraint verification. Fuses radar, lidar, IMU, and camera data with provable consistency guarantees.

capabilities

  • +Multi-sensor data ingestion and normalization
  • +Temporal alignment and synchronization
  • +Neural feature extraction from raw sensor streams
  • +Symbolic consistency checking across modalities
  • +Anomaly detection with formal bounds
( )

Agentic ROS2 Orchestrator

Robotics

AI agent that plans, monitors, and verifies ROS2 robot task execution. Uses neurosymbolic reasoning to ensure task plans satisfy safety constraints before deployment.

capabilities

  • +Task plan generation from natural language goals
  • +ROS2 node graph analysis and dependency checking
  • +Safety constraint verification (collision, workspace bounds)
  • +Real-time execution monitoring with symbolic watchdog
  • +Failure diagnosis with causal reasoning
// pipeline
How verification works.

Every lab uses the same neurosymbolic verification pipeline. The neural layer proposes, the symbolic layer proves.

verification-pipeline
Step 1: Neural inference
const proposal = await neuralLayer.analyze(input, {);
model: "gemma4:e4b",
domain: task.domain,
constraints: task.symbolicRules,
});
proposal is data-driven but unverified
 
Step 2: Symbolic verification
const result = symbolicLayer.verify(proposal, {);
consistencyCheck: true,
constraintSolve: true,
boundsCheck: true,
formalProof: task.requiresProof,
});
 
Step 3: Decision with audit trail
if (result.valid) {
emit({ verified: true, confidence: result.score, proof: result.proofId });
} else {
retry(input, result.violations);
}

Neural: perception and generation

The neural layer processes raw inputs using deep learning models. It recognizes patterns, extracts features, and generates candidate solutions. For VLSI, it understands RTL structure. For sensor fusion, it learns multi-modal embeddings. For robotics, it plans task sequences.

Symbolic: verification and proof

The symbolic layer defines formal constraints specific to each domain. Timing rules for VLSI. Consistency bounds for sensor fusion. Collision and joint limits for robotics. Every neural proposal must pass symbolic verification before being accepted.

// enterprise
Need a custom agent for your domain?

We build production-grade neurosymbolic agents for semiconductor, defense, and robotics companies. Every agent ships with formal verification and a complete audit trail.

Try Agent Builder -->View on GitHub