Project facts & technologies
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- Project name
- CAD Data Extraction — Intelligent CAD Drawing Data Extraction
- Client
- Reitz
- Industry
- Industrial Engineering & Manufacturing
- Use case
- Automatic extraction of dimensions, specifications, part numbers, and annotations from CAD drawings
- Core technology
- Gemini 2.5 Pro, LLM, Generative AI, Python, Flask, MongoDB
- Data accuracy
- 92% extracted data accuracy
- Overhead reduction
- 70% reduction in engineering overhead
- Quality control
- Validation checks on every extraction before records are accepted
- Stakeholder users
- Design engineers, manufacturing planners, design-review and documentation teams
Why is CAD drawing data so hard to get out?
CAD drawings are the source of truth for everything a factory builds — but the data inside them is locked in a visual format authored for human readers. Dimensions, tolerances, part numbers, material specifications, and annotations all have to be read off the drawing and re-keyed into downstream systems before design review, procurement, or manufacturing planning can proceed.
That re-keying is done by engineers — the most expensive and capacity-constrained people in the workflow. Manual extraction creates bottlenecks in design review and manufacturing processes, introduces transcription errors into specifications where errors are least affordable, and scales linearly with drawing volume. Every new project adds to the backlog.
What problem does the CAD extraction system solve?
Engineering teams at Reitz spent considerable time manually extracting dimensional data, specifications, and part information from CAD drawings — creating bottlenecks in design review and manufacturing processes. AiSPRY engineered the system around the realities of engineering documentation.
Key challenges
- Engineer-hours lost to transcription — skilled engineers spent their time reading drawings and re-keying data instead of engineering.
- Transcription errors in specifications — manual copying of dimensions and part numbers introduces errors exactly where precision matters most.
- Design-review bottlenecks — reviews and manufacturing planning stall while drawing data is extracted and structured.
- Dense, varied drawings — dimensions, tolerances, annotations, and title-block data appear in varied layouts that defeat rigid templates.
How does the CAD extraction system work?
AiSPRY developed a sophisticated GenAI system that reads CAD drawings end-to-end — extracting dimensions, specifications, part numbers, and annotations into structured records, validating every extraction, and storing results for downstream engineering and manufacturing use.
GenAI extraction core
- Gemini 2.5 Pro understanding — the LLM interprets drawings in context, reading dimensions, tolerances, part numbers, and annotations the way an engineer does
- Structured output — extracted data is normalized into consistent, queryable records stored in MongoDB
- Flask web platform — engineers upload drawings and receive validated, structured data through a simple web workflow
Quality validation
- Validation checks on every extraction — completeness and consistency rules catch questionable extractions before records are accepted
- 92% data accuracy — extraction quality that replaces manual re-keying rather than just assisting it
- 70% less engineering overhead — engineers review flagged exceptions instead of transcribing every drawing
See CAD data extraction in action
A walkthrough of the CAD Data Extraction platform — drawings in, Gemini-powered extraction of dimensions, specifications, part numbers and annotations, validation checks, and structured records out.
CAD Data Extraction — drawings to structured engineering data
- Engineer-grade reading — dimensions, tolerances, part numbers, and annotations extracted in context
- Validation built in — every extraction passes quality checks before acceptance
- Structured, queryable output — records land in MongoDB ready for design review and manufacturing planning
- Measured outcomes — 92% data accuracy and 70% engineering overhead reduction
How does the system handle drawing complexity and data trust?
Engineering drawings demand precision that generic document AI cannot guarantee. AiSPRY engineered around three constraints — drawing variety, extraction precision, and the trust required before extracted data drives manufacturing decisions.
Engineering constraints
- Drawing variety — LLM-based understanding adapts to varied layouts and notation conventions without per-format templates
- Precision-critical fields — dimensions and part numbers are extracted with context awareness, distinguishing similar-looking fields that rigid OCR confuses
- Data trust — validation gates ensure engineers review exceptions rather than re-checking every record, keeping accuracy at 92% without re-introducing the manual bottleneck
What measurable results does the system deliver?
The platform removed manual drawing transcription from Reitz's engineering workflow — and moved both headline metrics decisively.
Headline outcomes
- 92% data accuracy — validated, structured extractions replace error-prone manual re-keying
- 70% engineering overhead reduction — engineers review exceptions instead of transcribing every drawing
- Faster design review — drawing data is available to reviews and manufacturing planning without transcription delay
- Cleaner downstream data — consistent structured records flow into engineering and manufacturing systems
CAD Data Extraction — frequently asked questions
Below are the most common questions about how the platform works, what it extracts, and the results it delivers.