Bruviti
Use case

Parts Identification from Blueprints

AI-powered blueprint analysis that auto-extracts technical specifications and mechanical parts from engineering drawings with 80% accuracy, standardizing cataloging across systems.

Challenge

Engineering teams manually review hundreds of drawings daily to extract dimensions, GD&T, materials, and identify components. Each drawing takes 15–20 minutes across CAD viewers, specification databases, PLM PDM, and ERP. Manual entry causes inconsistent metadata, missed tolerances, duplicate parts, and compliance risk.

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The objective

Auto-extract ≥80% of technical specifications and mechanical parts from engineering drawings with accurate metadata capture and standardized cataloging across systems to further allow text+image based search.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied OCR and computer vision models. Training data came from thousands of technical drawings, specification sheets, parts catalogs, and enterprise standards. This enabled the AI agent to recognize drawing templates, extract dimensions and tolerances, parse GD&T symbols, identify mechanical parts, and map metadata fields.

Agent Training workflow diagram showing training data collection, vision tool configuration, and AI capabilities development TRAINING DATA Drawings Standards Parts catalogs Enterprise data Model suite Trained skills Spec extraction Part recognition Metadata mapping

Workflow orchestration

The AI agent reads new drawings, detects template types, and determines the correct path: detect tables and views, parse title blocks, extract dimensions with tolerances, parse GDT, and identify mechanical parts. It processes CAD drawings, PLM, ERP, the ontology, and a vector index for similarity search. Orchestration branches when rules apply, for example, when a drawing revision is superseded or when BOM and drawing data conflict, while ensuring ISO compliance and auditability.

Blueprint Agent Workflow orchestration diagram SYSTEMS Drawings Blueprint agent CAD viewer PLM ERP Ontology Vector Index

Execution and resolution

At inference time, based on input query with a part photo and description, the AI performs multi-modal similarity matching against all drawings and detected mechanical components, evaluating visual patterns, dimensions, tolerances, materials, and GD&T specifications. The system delivers re-ranked results in under 30 seconds with matched components highlighted and enables side-by-side comparison of multiple drawings to highlight dimensional differences and specification variations. Ambiguous matches are flagged with alternatives while the system continuously learns from user selections to improve search accuracy.

Blueprint Library Execution workflow diagram User QueryImage + Text Classification MetadataExtraction Visual Analysis Similaritysearch AgentValidation Loop Results

Results

85%Auto extraction accuracy
<30 secPer search query
400+ hrsEngineering hours saved per month

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Frequently asked questions

How does AI extract part information from engineering blueprints?

AI uses optical character recognition and computer vision to scan engineering drawings, automatically detecting dimensions, tolerances, GD&T symbols, and material specifications. The system parses title blocks, identifies mechanical components, and maps extracted data to standardized catalog entries, replacing hours of manual review per drawing.

What is multi-modal search for spare parts identification?

Multi-modal search lets users query a parts database using both images and text simultaneously. AI compares visual patterns, dimensions, and specifications against indexed engineering drawings and catalogs to find matching components. This approach handles worn labels and partial descriptions that text-only search would miss.

How can service teams standardize parts metadata across systems?

AI-driven extraction creates consistent metadata by applying uniform rules to every engineering drawing processed. The system normalizes naming conventions, validates tolerances against standards, deduplicates entries, and synchronizes records across PLM, ERP, and catalog systems, eliminating the inconsistencies caused by manual data entry.