TrigGuard
TRIGGUARD AUTONOMY
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AI Governance for Autonomous & Industrial Systems

Autonomy stacks chain perception, planning, and actuation. The industry-standard approach to hazardous machinery is interlocks, TrigGuard provides an execution authorization layer so unsafe or out-of-policy actions never reach motors, brakes, weapons interfaces, or mission payloads.

Problem & risk

Validation alone cannot cover open-world operation. When models propose trajectories, force, or communications, safety cases require deterministic gates, traceable decisions, and independence between the learning system and the final actuation path.

Engage our autonomy safety team for architecture review.

Regulatory context

Safety-critical software (e.g. ISO 26262, DO-178C) and defence AI ethics guidance expect traceable control of automated behaviour and human oversight where required.1

  1. Align TrigGuard evidence packs to your notified body / authority expectations; we provide decision records, not product certification.

Solution

TrigGuard enforces policy between planning outputs and low-level controllers: PERMIT only when constraints hold; DENY or SILENCE otherwise, with receipts suitable for incident review and assurance.

  • Deterministic evaluation for real-time loops
  • Separation between model and actuation
  • Integration via APIs/SDKs for robotics stacks

Integration points

Typical interfaces: motion planners to controller bridges, fleet command systems, simulation-to-hardware promotion gates, and secure telemetry for human-on-the-loop approvals.

Execution surfaces in autonomous & industrial systems

Industrial and robotics buyers search for concrete control points: motion, missions, lines, and promotion to hardware. This section maps those paths to governance spokes without duplicating the Solution narrative above.

  • Robotics and AMR missions Fleet planners that assign routes, docks, or manipulator sequences are agent-like. AI agent safety constrains tools, states, and handoff to controllers.
  • PLC and line control Closed-loop changes to speed, torque, or interlocks need deterministic gates. Fail-closed AI systems keep the plant safe when models or telemetry disagree.
  • Warehouse and intralogistics automation Pick, pack, and sort decisions that release inventory or equipment should pass pre-execution authorization before irreversible motion.
  • Vision and quality systems driving rejects When classification feeds divert, rework, or scrap lines, treat outcomes as policy-bound decisions. Deterministic authorization preserves repeatability for quality and safety cases.
  • Fleet and yard autonomy Vehicles and shuttles crossing safety envelopes need bounded autonomy and evidence. Combine agent safety with AI decision verification for audit trails.
  • MES and ERP triggered shop-floor actions Enterprise workflows that open work or release batches should inherit policy enforcement at the execution API, not only in MRP screens.

Next steps

Choose how you want to engage, each action logs intent for follow-up when analytics is enabled.

Meet our Autonomy Safety Team Request Robotics Safety Demo Explore Interlock API for Mobile Robots

Long-form articles on the content calendar can deep-link here as they ship.