AI for AI — Neuroevolutionary Video Agents

Grow Private, Physics‑Aware Video Agents from Experience

Parent agents spawn specialized child agents using NEAT/Multiagent HyperNEAT. Agents learn from enterprise video streams, evolve with grounded rewards, and improve continuously with human feedback — all on your private data estate.

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On‑prem / Edge‑first Data never leaves Human‑in‑the‑Loop
Parent → Child Evolution

NEAT lineage producing specialized video agents

Parent Agent NEAT + Fitness Child Agent 1 Mutate + Crossover Child Agent 2 Mutate + Crossover Child Agent 3 Mutate + Crossover Specialize Manufacturing Retail Airports
Fitness‑aligned KPIs
OEE ↑, F1 ↑, SLA ↑
Deployment targets
Edge, GPU, VMs
Feedback
Corrections, ratings, demos
Why now?

Experience over Imitation

Streams of Experience

Agents learn continuously from long‑horizon operations. Evolve controllers from rolling video and telemetry streams rather than short episodes.

  • Continuous Learning: Real-time adaptation from operational data
  • Long-horizon Optimization: Focus on sustained performance improvements
  • Multi-modal Streams: Video + telemetry + sensor fusion
Grounded Rewards

Optimize real outcomes, not opinions. Fitness is shaped by measurable signals: OEE, defect F1, shelf‑stock accuracy, queue times, SLA adherence.

  • KPIs: OEE, F1 scores, SLA compliance
  • Operational Metrics: Queue times, throughput, quality
  • Business Outcomes: Cost reduction, efficiency gains
Plan + Reason

Combine evolutionary policy search (NEAT/HyperNEAT) with model‑based planning for robust, explainable behaviors that transfer from sim→real.

  • Evolutionary Search: NEAT/HyperNEAT for policy optimization
  • Model-based Planning: World models for decision making
  • Sim-to-Real Transfer: Robust, explainable behaviors
Industries

Purpose‑Built Agents for Your World

Transform your operations with AI-powered neuroevolutionary agents across multiple industries

Assembly & Quality Agents

See‑Think‑Act on the production line with evolved intelligence. Real-time assembly guidance, defect detection, and skill transfer capabilities.

Shelf, Queue & Loss‑Prevention Agents

Always‑on floor intelligence with evolved pattern recognition. Inventory management, queue optimization, and loss prevention capabilities.

Turnaround & Baggage Agents

Airside awareness with SLA-first evolved intelligence. Baggage tracking, turnaround optimization, and safety management capabilities.

Domain-Specific Agents

Evolved agents for your unique business requirements. Custom training, adaptive learning, and continuous evolution capabilities.

Under the hood

How Neuroevolved Video Agents Are Born

A sophisticated process combining evolutionary algorithms with continuous learning from real-world data

Evolutionary Core

NEAT → Multiagent HyperNEAT → CPPN patterns

We represent each agent controller as a neural topology. A parent agent encodes a policy; mutation & crossover spawn child agents that explore architectures and weights. HyperNEAT scales this by evolving a CPPN that generates related policies across an agent team using task geometry.

🔧 Key Evolutionary Mechanisms

🌐 NEAT: Evolves both network structure and weights simultaneously
📊 HyperNEAT: Scales evolution using Compositional Pattern-Producing Networks
👥 Multiagent Coordination: Evolves policies for teams of agents working together
⚡ Real-time Adaptation: Continuous evolution during live operations 
// NEAT-driven agent evolution
for (gen of generations) {
  population = mutateAndCrossover(population)
  evaluate(population, fitnessSignals) // OEE, F1, SLA, QoS...
  population = selectTopK(population)
  if (needsTeamPolicy) {
    cppn = evolveCPPN(taskGeometry)
    population = instantiateTeamPolicies(cppn)
  }
}
Topology + Weight Evolution
Indirect Encoding via CPPN
rtNEAT for Continuous Ops
Model-Based Planning

Private by Design

Enterprise‑grade security & compliance

Our platform is built with enterprise-grade security at its core. Every component is designed to maintain data sovereignty, ensure compliance, and provide robust access controls while enabling seamless human-AI collaboration.

🔒 Security & Compliance Features

📜 SOC 2 Type II: Certified compliance with industry security standards
🛡️ GDPR & CCPA: Full compliance with data privacy regulations
🔐 Zero Trust Architecture: Continuous verification of all access requests
📋 Audit Trails: Complete logging of all system activities
Data Sovereignty

Data stays on‑prem / VPC; no third‑party sharing or cloud dependencies

Encrypted Infrastructure

End-to-end encryption for video lakes, model lineage, and role-based access control

Human Feedback Loop

Secure human‑in‑the‑loop feedback: correction, ranking, critique with full audit trails

CI/CD for Agents

Secure agent versioning, A/B testing, and shadow deployments with rollback capabilities

Threat Detection

Advanced anomaly detection and automated threat response for agent behaviors

Latency
<100ms Edge Prompts
Drift Monitors
Continuous

Agent Creation Flow

1

Ingest & Label

Connect cameras, import historical video, bootstrap with weak labels and heuristics.

2

Frame Goals → Fitness

Map KPIs (OEE, F1, SLA, queue time) into a composite, grounded reward.

3

Evolve

NEAT/HyperNEAT explore architectures; world‑model guides sample‑efficient search.

4

Human Feedback

Operators correct, rank, and add demonstrations → experiential learning.

5

Sim → Real

Validate in digital twins, then progressive rollout with guardrails and auto‑rollback.

6

Continual Learning

rtNEAT keeps agents improving under live drift, seasonality, and shifts.

Launch Agent Studio

World‑Model Assisted Evolution

Plan actions by simulating consequences

Crisp Technical: Agents learn a world dynamics model from continuous observation–action streams, forecasting rewards and constraint violations. Model-guided evolutionary search prunes weak policies and generates counterfactual rollouts that transfer to real-world execution.

Physics‑Aware Priors

From pose/force proxies and real-world constraints

Safety Envelopes

No‑go zones and constraints baked into fitness

Counterfactual Planning

For rare events, incidents, and edge cases