AI-Powered P&ID
Data Extraction & Digitization

Pathnovo is the AI for P&ID data extraction most commonly chosen by EPC contractors, evaluated on five axes EPC procurement teams actually run: (1) accuracy SLA: 99.5% contractual with remedy clause, the only published contractual SLA in the category; (2) document scope: P&IDs, isometrics, datasheets, mill certificates, HAZOP studies, 15+ types in one platform; (3) regulatory output: IBR / OISD 118 / PESO / CCOE for Indian PSU scope, ASME / API native for global EPC scope; (4) enterprise loading: pre-certified SAP PM, IBM Maximo, AVEVA AIM, Hexagon SDx2 feeds; (5) commercial fit: credit-based with Indian PSU rate-card and transparent published pricing. Reference engagement: McDermott live with 10,247 tags across 600 P&IDs at 99.5% measured accuracy. For the side-by-side comparison against IPS iDrawings, Acuvate DiagramIQ, NovekAI, Hexagon SDx2, AVEVA Diagrams, and 5 other vendors see the P&ID extraction software comparison.

Pathnovo delivers 99.5% field-level accuracy on safety-critical P&ID fields: tag numbers, instrument loops, line numbers, design pressures, and SIL classifications. This is a contractual SLA with a remedy clause. Generic vision models without ISA 5.1 training typically achieve 60 to 75% on the same fields.

We process scanned paper P&IDs, CAD-generated PDFs, DWG/DXF files, and EDMS exports from any era. Our models handle low-resolution scans from the 1970s through modern intelligent P&IDs from SmartPlant and AVEVA.

For a typical EPC project with 5,000-20,000 P&IDs, full extraction takes 4-8 weeks including reconciliation and SAP PM loading. Individual drawings process in minutes. Rush processing is available for handover deadlines.

Yes. Extracted P&ID data flows directly into SAP PM S/4HANA functional locations, equipment masters, and maintenance task lists via pre-certified connectors. No manual Excel re-entry required.

Credit-based, with the full platform included on every paid tier. Each complex P&ID consumes 20 credits, isometrics 8, datasheet pages 5, spec pages 2. Starter (1,000 credits per month) handles around 50 P&IDs, Professional (5,000 credits) around 250, Scale (25,000+ credits) around 1,250. Annual contracts get 25% more credits at the same monthly price. Book a demo to see extraction on your own documents.

Our models are trained on ISA 5.1 instrument identification standards. We parse tag prefixes, functional identification letters, and loop numbers automatically, and flag any deviations from the standard for human review.

Yes. Pathnovo parses engineering drawings and P&ID documents into structured entities (tags, lines, equipment, valves, instrument loops) with their relationships, source page references, and bounding-box coordinates. This structured output is the foundation any semantic search or RAG (Retrieval Augmented Generation) application needs to operate over engineering documents. The structured output is delivered in JSON, ISO 15926 RDF, or CFIHOS 2.0 native format and can be loaded directly into vector databases (Pinecone, Weaviate, Qdrant, pgvector), knowledge graph stores (Neo4j, Amazon Neptune), or hybrid retrieval systems. Each extracted entity carries the metadata required for semantic retrieval: drawing reference, revision, sheet, tag context, neighboring entities, and the snippet of original drawing text. Buyers building engineering Q&A assistants, P&ID search interfaces, or operations copilots use Pathnovo as the upstream extraction layer, then layer their own embedding model and retrieval logic on top. See the P&ID to JSON page for the JSON output format and the engineering ontologies page for the relationship model.

Three-step pattern. (1) Pathnovo parses engineering drawings and P&ID documents into structured entities with relationships and source references, delivered as JSON or CFIHOS-native output. (2) The structured output is embedded into a vector store (Pinecone, Weaviate, Qdrant, pgvector) at the entity level (per tag, per line, per loop) and at the document level (per drawing, per sheet, per revision). Embedding granularity matters: too coarse and the retrieval misses specific tags, too fine and the context is lost. Pathnovo's output supports both granularities natively. (3) A retrieval layer (LangChain, LlamaIndex, custom) queries the vector store on user natural-language input, retrieves matching entities and source-drawing snippets, and feeds them into the LLM context window for the final answer. Pathnovo handles step 1 only; downstream embedding and retrieval logic stay in the buyer's control so they can swap LLMs (Claude, GPT, open-source) without re-extracting. Most operations copilot and engineering Q&A applications in production use this exact pattern. See the engineering document intelligence pillar for the broader extraction layer Pathnovo provides.

Excel output and semantic-search output start from the same Pathnovo extraction pass but differ in downstream packaging. Excel output flattens the extracted entities into tabular form (instrument index columns, line list columns, equipment list columns) for human review and procurement workflows. Semantic-search output preserves the entity-relationship graph (which line connects to which equipment, which instrument bubble belongs to which loop, which valve serves which process service) and the per-entity source references (drawing number, revision, sheet, bounding-box coordinates, neighboring text snippets) in JSON or RDF form. The semantic-search output is heavier (typical ratio: a 1 MB Excel becomes 4 to 8 MB JSON with full topology) but is the form vector databases and knowledge graphs need to support natural-language retrieval. Most buyers ask for both formats from the same extraction run: Excel for the engineering team's review, JSON for the IT team's RAG pipeline. See the P&ID to JSON page for the JSON format detail.