The Lifecycle Synapse
Integrated Product Support, rewired
Decades of support knowledge, locked in documents — rewired into a living graph.
Axon reads your legacy ILS archive and turns it into a standards-compliant knowledge graph your engineers can question, trace, and build on, from ingest to S-Series publication.
01 · INGEST
Bring it in.
Manuals, catalogues, FMECAs, records: any format, dual-engine OCR.
02 · EXTRACT
Read it.
AI lifts typed entities and relationships, each traceable to its source.
03 · MODEL
Connect it.
Fuse documents into one graph; deconflict what they disagree on.
04 · ANALYSE
Question it.
Graph-aware AI, grounded search and impact analysis, with citations.
05 · REPORT
Publish it.
S-Series artefacts and data exports, generated at the click of a button.
Why it matters
A platform outlives the people and systems that documented it.
Decades of manuals and catalogues, answerable in seconds, not weeks of searching.
Assets, tasks, failures, spares and publications, linked the way the system actually works.
Every fact carries its source document, page and the exact line it was read from.
Grounded in the S-Series standards: structurally, not as a label applied afterwards.
The knowledge to support a platform for thirty years is already written, scattered across legacy ILS archives no one can fully read. Axon turns it into a living, traceable, standards-compliant graph: the digital twin of your product support.
01 · INGEST
Bring in everything.
Dual-engine ingestion
Every source, every format.
Maintenance manuals, provisioning catalogues, FMECAs, drawings, spreadsheets. All in.
Two OCR engines read the hardest defence documents: Azure Content Understanding for pixel-exact bounding boxes, Mistral Document AI for dense, multi-column tables. Both normalise to one schema, with line-level confidence you can review before anything moves on.
100–500+entities from one 50-page manual
Ingest queue
Dual-engine OCR
Pixel-exact bounding boxes: overlay every entity on the source page.
Dense, multi-column layouts and tables, into clean structured markdown.
Confidence you can see
02 · EXTRACT
Read like an engineer.
AI extraction
Text becomes typed knowledge.
13 entity types, 14+ relationships, every one traceable to its source.
The model reads each page and lifts the structure an engineer would: assets, components, failure modes, spare parts, tasks and hazards, and the relationships between them. Every entity keeps its page number and the verbatim line it came from.
From the page
…and how they relate
Traceable to source
03 · MODEL
One connected graph.
A growing knowledge graph
Multi-document fusion
Deconfliction engine
Graph · Fusion · Deconfliction
Many documents. One source of truth.
Fuse every document into a single graph, and resolve what they disagree on.
As documents commit, the graph grows. The fusion engine merges the same asset described in five places into one node, matching on NSN, Part Number + CAGE and LCN. The deconfliction engine then settles conflicting values by confidence and provenance. Nothing merges without human sign-off.
1,140relationships in the demo graph
See Axon read a document you struggle with today.
Bring us a manual, an IPC or an FMECA. Watch it ingest, extract, and connect. Live.
04 · ANALYSE
Question your fleet.
Graph-aware AI
Answers, grounded in your graph.
Ask in plain language. Get an answer with citations, and the impact mapped out.
Smart search and reasoning run over the graph, not a guess: every answer cites the document, page and node it came from. Trace a failure across subsystems, run an impact analysis before a change, and pin what matters to build an evidence trail.
Grounded AI chat
Impact analysis
Evidence pinboard
05 · REPORT
Publish in a click.
Content Studio templates
Generated, grounded, cited
DMC-AXON-A-04-21-0000-00A-040A-A
Mk3 Gearbox — Description & Operation
cited: PropulsionSystems_v3 · p.42 cited: IPC_Mk3 · item 04Export anywhere
One artefact, many destinations
Documents
Structured data
Content Studio
From graph to artefact.
S1000D data modules, FMECAs, IPCs, task analyses, training: generated from grounded data.
Pick a template and Content Studio drafts a formal S-Series artefact straight from the graph, every claim cited back to source. Then export to Word, PDF, XML or HTML, or as structured data your PLM and ERP can ingest.
7 templatesS-Series, multi-format export
Built for S-Series
Compliance is structural, not decorative.
The S-Series specifications don't just label the output; they govern what gets extracted, how entities resolve, and which relationships are valid.
SX000i
IPS FrameworkSystem identity and Logistic Support Analysis governance: the backbone of the whole programme.
S1000D
Technical PublicationsModular documentation via Data Module Codes, generated rather than retyped.
S2000M
Material ManagementProvisioning and cataloguing: NSN, part numbers and CAGE codes resolved automatically.
S3000L
Logistic Support AnalysisProduct breakdown, task analysis and the support resources each task needs.
S5000F
In-Service FeedbackFailure reporting, FMECA and reliability: the loop back from the fleet.
S6000T
Training AnalysisCompetencies, skill codes and training packages, traced to the tasks they support.
What it's worth
A sovereign, living digital twin of your support data. ILS-to-IPS migration in weeks, not years. Every decision traceable to source, every artefact standards-compliant and audit-ready by default.
Turn the support-data package you owe your customer into a repeatable, automated pipeline. Generate S-Series deliverables from grounded knowledge and ship them faster, with the evidence trail built in.
Weeks, not months. Traceable, not trusted-on-faith. A living digital twin of your product support, within reach of teams that could never build it before.
Built for serious programs
Enterprise-grade, sovereign by design.
Deployable to the Azure region your programme requires, with data residency aligned to national rules. Sovereignty built in, not bolted on.
AI proposes; a person reviews, corrects and commits, at every stage from OCR to the committed graph. Nothing enters without human sign-off.
Azure OpenAI never trains on your content; inference is isolated to your deployment. Full provenance from source line to graph node.
- Source-traceable end to end
- Human-reviewed
- No model training on your data
- Classification-aware
- Role-based access
- Sovereign Azure (AU)
See Axon read your archive.
Bring us a stack of manuals you can never search properly: a manual, an IPC, an FMECA. We'll show you how Axon reads them, links them, and answers questions against them, with every response cited to source.
Thanks, we'll be in touch.
A member of the team will reach out shortly.