Cateo turns field context into an initially AI-generated troubleshooting guide, part-aware artifact package, and reusable engineering record. The first pass is clearly labeled as AI-generated. Users can accept it, or decline it with a reason so Cateo reevaluates the full submission and improves later revisions through crowdsource history.
| PROCEDURE | SYSTEM | VERSION | STATUS | UPDATED |
|---|---|---|---|---|
| Troubleshooting guide | Diagnostic analyzer | REV 4 | APPROVED | AI generated, then crowd-updated |
| Sample path isolation | Clinical automation | REV 2 | REVIEW | Awaiting stronger field evidence |
| Motion stage restart | Packaging cell | REV 1 | DRAFT | Initial release candidate |
Cateo keeps the troubleshooting guide connected to part metadata, document classes, lifecycle states, effectivity, external system IDs, change history, and relationship links so retrieval does not depend on one lucky keyword search.
The primary navigation now routes users through one consistent Cateo model: start a request, inspect released knowledge, open the tools hub, and manage the account workspace from the user menu instead of hopping between different shells.
Public product pages use clear headings, structured metadata, and technical copy shaped for engineering search intent, while private troubleshooting content remains protected from indexing.
Cateo converts engineering inputs into structured artifacts such as troubleshooting plans, inspection checklists, standard operating procedures, and evidence-backed reports.
No. Cateo augments engineers by structuring their reasoning and accelerating diagnostics, while keeping the human in control of decisions and validation.
Cateo is designed as a local-first system where models can run on your machine, ensuring data privacy while still allowing optional cloud integrations.
Cateo is built for field service, manufacturing, calibration labs, diagnostics, and other engineering environments where structured troubleshooting and inspection workflows are critical.