How noeud.ai reads technical documents.
A three-stage pipeline that detects visual elements, parses document structure, extracts domain-specific entities, and assembles searchable knowledge graphs — without pretending PDFs are just long strings of text.
Three stages. One coherent extraction.
Stage 1 — Visual Detection
- Layout analysis using DocLayNet-trained models
- Table, figure, diagram, and label zone identification
- YOLO-based visual element detection
- Page segmentation into structural regions
- Coordinate-level precision for every detected zone
Stage 2 — Structural Parsing
- OCR with context-aware text assembly
- Table structure reconstruction
- Hierarchy detection (sections, subsections, procedures)
- Cross-reference linking within and across pages
- Visual-to-text grounding for diagram labels
Stage 3 — Knowledge Assembly
- Domain-specific entity and relationship extraction
- Knowledge Unit normalization
- Evidence gate validation (text + visual proof)
- Knowledge graph construction with linked facts
- Searchable output via REST API
Watch the pipeline in action.
From raw technical documentation to structured, searchable knowledge.
Why it works on real technical content.
Design decisions that matter when documents are dense, structured, and full of domain-specific meaning.
Visual Evidence Gate
Every extracted value from a diagram requires both a text-channel and a visual-channel confirmation. If both don't agree, the fact is flagged — not silently accepted.
Domain ontologies
Each domain uses a dedicated extraction ontology, built from real document corpora. Not a generic NER model retrained on Wikipedia.
Multi-LLM backends
Extraction uses the best model for each subtask — Gemini for visual grounding, Claude for structured reasoning, GPT-4o for specification parsing.
Knowledge Unit normalization
All extractor outputs converge into a unified Knowledge Unit format before indexing — regardless of source document type or extraction backend.
Regression harness
Every pipeline version is tested against golden-set documents. Extraction quality is measured, not assumed.
Domain pack builder
New domains are bootstrapped automatically. Feed a PDF corpus, get an extraction ontology with 9 LLM-generated components in under 30 minutes.
What noeud.ai does not do.
A serious platform earns trust by being clear about its scope. Here is where ours ends.
Not a document management system
noeud.ai extracts knowledge from documents. It does not store, version, or manage your document library. Use it alongside your existing DMS.
Not a general-purpose chatbot
The platform answers questions grounded in extracted technical knowledge. It does not improvise answers from general training data.
Not a replacement for domain expertise
It makes documentation searchable and structured. It does not replace the engineer who understands what the extracted facts mean in context.
Knowledge lives in more than just PDFs.
Technical expertise is captured in multiple formats. The pipeline extracts structured knowledge from all of them.
Technical PDFs
Machine manuals, datasheets, service procedures, specification sheets, and engineering documentation of any length and complexity.
Training Videos
YouTube tutorials, operator training recordings, manufacturer demonstrations, and technical webinars. The pipeline transcribes, segments, and extracts knowledge units with timestamp references.
Technical Presentations
Slide decks, conference talks, product launch materials, and internal training content. Structured knowledge extracted from slides, speaker notes, and visual diagrams.
Mobile Capture
Photos of HMI screens, error codes, and machine components captured by field technicians via the noeud.ai iOS app. Visual diagnostics and knowledge enrichment from the shop floor.
Remote Diagnostics
Live camera feeds accessible from anywhere. Real-time alarm forwarding to mobile devices. Full diagnostic capability and collaborative support — the entire plant travels in your pocket.
Continuous Vision Monitoring
Cameras mounted on HMIs and control panels provide 24/7 real-time analysis. The system detects anomalies, alarm patterns, and parameter drift before operators notice — and cross-references with the knowledge graph for instant context.
See the pipeline on your documents.
Upload a technical PDF and see what structured knowledge comes back. No commitment, no sales deck — just results.