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Industrial Diagnostics Copilot ​

The flagship buyer workflow. Turn machine evidence into a maintenance decision with a replayable incident record.

Status

Demo-ready showcase. Synthetic scenarios are generated from local industrial/predictive checkpoints — this is a coherent operational demo, not live plant integration.

Canonical value proposition ​

Qriton turns machine evidence into maintenance decisions with a replayable incident record.

The differentiating combination:

text
prediction + uncertainty + recorded decision trajectory + input/model hash

What the demo proves ​

A buyer or maintenance lead can walk through a plant incident end to end:

  1. Sensor and inspection evidence captured from the scenario
  2. HLM diagnosis with prediction and uncertainty
  3. Risk and action state derived from the diagnosis
  4. Safety state for operator review
  5. Audit packet with input hash, model hash, and recorded trajectory
  6. Exportable incident report in printable Markdown / HTML
  7. ROI framing for the maintenance decision

What the buyer sees ​

  • Diagnosis with attached uncertainty and basin signals
  • A printable validation artifact with buyer review questions
  • An incident report index across scenarios (chemical compressor seal wear, others)
  • A scenario manifest documenting checkpoint provenance for every diagnosis

How it differs from a transformer chat ​

The same inference object that produces the diagnosis also produces the auditable trajectory. A regulator can replay the run and verify the hash — that is not something attention scores natively expose.

Caveats ​

  • Packaged scenarios are synthetic showcase cases. Present as a coherent operational demo, not as live plant integration.
  • ROI numbers are illustrative; replace with buyer-specific inputs in a pilot.
  • Replayability depends on bundled hashes; partner data is handled separately.

Where it fits ​

This is the right opening demo for:

  • Industrial buyers (maintenance, reliability, plant operations)
  • Compliance reviewers wanting a hash-pinned decision trail
  • Engineering leads benchmarking the auditability story end to end