AI that understands maintenance work, the documents behind it, and the stakes of getting it wrong.
Delivers step-by-step repair guidance, knowledge checks, suggested fixes, on-demand learning, and shift logs. An agentic system that understands intent and returns source-backed, cited responses — so maintenance organizations execute with higher quality and efficiency.
Consistent, AI-generated shift logs improve communication between maintainers. The result: less rework, faster resolution, higher productivity across every shift transition.
Custom quizzes, flash cards, and explanations generated on demand from your own documentation. Faster onboarding, broader skill coverage, and durable knowledge retention across PCS cycles.
Load every TO, SOP, Service Bulletin, and reference source into one workspace. AI-extracted keywords and concepts auto-generate a custom knowledge graph for each fleet — so nothing is siloed, and every query draws on the full corpus.
Version history, stale-content alerts, archival workflows, and citations bound to the exact reference version used. Answers come only from approved sources — 100% traceable, zero hallucinations.
Maps equipment, components, fault codes, tools, and procedures for context-aware troubleshooting. Fine-tuned extraction plus OCR and vision models read diagrams and charts inside large, complex technical manuals.
Fully localized, secure on-device deployment for forward-deployed teams in disconnected or low-connectivity environments. DDIL-native means disconnection is the design baseline — not an edge case.
Beta — available on request
Sensitive data stays private and is never used to train models. Built on AWS with FedRAMP and ATO in process. Containerized architecture aligned to ASGARD DevSecOps posture for accelerated authority-to-operate.
We'll walk your team through a live demonstration on representative documentation, review your sustainment workflow, and outline a path to pilot — including transition via PEO-TIS and ASGARD.