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Bruviti
Use case

Parts Catalog Compiler

Agent automation that generates interactive catalogs from 3D models and BOMs with 70% or greater reduction in authoring time, 90% or greater auto-link coverage across parts and documents, and technician search that supports UTID serial and natural language with under 2 seconds query latency.

Challenge

Building interactive parts catalogs is slow and manual. Teams juggle Illustrator masking, hierarchy building, linkages to BOMs and documents, and repeated validations across disconnected tools in PLM/PDM, DAM, spreadsheets, 3D models, and manuals. Existing assets are underused. Technicians lack UTID serial lookup and natural language search across parts and procedures, causing longer field time and rework. Each model release demands many tool hops and dozens of hours to produce and validate the catalog.

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The objective

Generate interactive catalogs from 3D models and BOMs with ≥70% reduction in authoring time, ≥90% auto-link coverage across parts and documents, and technician search that supports UTID serial and natural language with ≤2 seconds query latency.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied its vision model for segmentation, detection, and OCR, NLP retrieval for semantic search, a policy model for authoring and validation rules, and predictive ranking for disambiguation. Training data came from 3D CAD and exploded view renders, historical Illustrator files and masking rules, BOM spreadsheets, service manuals and procedures, UTID serial mapping tables, technician clickstreams, and annotated screenshots. This enabled the AI agent to recognize UTIDs and serials, model and revision, part IDs and BOM lines, 3D meshes and layers, SVG region masks, document IDs, and localization metadata.

AI Models and Training Data workflow diagram DATA SOURCES AI LAYER TRAINING BOM Excel 3D models Illustrator files Service manuals UTID serial map Clickstreams Vision model NLP search model Policy model Ontology graph

Workflow orchestration

The AI agent analyzes authoring requests and change events, extracts model identifiers, and routes work across ingest, mapping, validation, and publishing. It navigates PLM/PDM, DMS/DAM, ERP, catalog CMS, render services, and search indexers, mirroring a content specialist workflow. Orchestration branches on confidence and policy—for example, when part-to-BOM matching is below threshold or duplicate names are detected—while versioning assets, enforcing checks, logging audits, and routing low-confidence items to human queues.

Parts Catalog Compiler Solution Architecture diagram AI LAYER ENTERPRISE APPS Ontology graph Orchestration agent PLM PDM DMS DAM ERP Catalog CMS Render service Search index Audit log Human queue

Execution and resolution

The AI agent executes end to end: import 3D and BOM, align parts to BOM entities, auto-generate the assembly hierarchy, create region masks and SVG hotspots, detect and label parts in images, link parts to procedures and documents, build semantic and UTID serial indexes, run coverage and naming validations, and publish to the catalog CMS. Runs complete in minutes with updates posted across connected systems. Exceptions such as missing CAD, mismatched IDs, ambiguous links, or duplicate SKUs are escalated to subject matter experts with diffs and suggested fixes attached.

Workflow Execution diagram Import modelsand BOM Align partsto BOM Build assemblyhierarchy Createregion masks Detect andlabel parts Link partsto docs Build searchindexes Runvalidations Publish tocatalog SME queue

Results

70%Reduction in catalog authoring time
<2sQuery latency with 40% faster technician lookup
≥90%Auto-linked coverage across parts, BOM, and documents

Frequently asked questions

How are interactive parts catalogs generated from 3D models?

AI processes 3D CAD models and bill-of-materials data to automatically build interactive catalogs with clickable hotspots, exploded views, and linked documentation. The system segments assemblies, maps parts to BOM lines, generates SVG overlays, and indexes everything for natural language search, reducing catalog authoring time by 70% or more.

What is automated part-to-document linking in parts management?

Automated part-to-document linking uses AI to match individual parts with their related service manuals, installation procedures, and specification sheets. The system reads part identifiers, serial numbers, and descriptions to create cross-references automatically, giving field technicians instant access to relevant documentation during repairs.

How does semantic search improve spare parts lookup for technicians?

Semantic search understands the meaning behind technician queries rather than relying on exact keyword matches. Technicians can describe a part by function, appearance, or symptoms, and the system retrieves relevant results from catalogs and BOMs. This natural language capability reduces lookup time and helps less experienced technicians find correct parts faster.