If you’ve ever tried to feed an engineering schematic, a CAD export, or a complex blueprint into AI/ML pipelines, you know the pain

You send a high-density visual document to a Vision-Language Model (VLM), and it gives you a great summary: "This is a diagram showing a system with labeled components and a table of specifications." That's useful for a demo, but it's useless for a production-grade system that needs to answer: "What is the exact value in cell B7 of the specifications table for component XYZ?"

To move from "cool demo" to "operational ROI," we need to stop asking AI to summarize documents and start asking it to reconstruct them with full structural fidelity.

Why Technical Drawings Break the Standard Document Stack

Most document processing pipelines treat a page as a linear flow of text. Technical drawings aren’t linear—they’re high-density ecosystems where meaning comes from structure, position, and context.

Here’s what makes them especially hard:

• Spatial Context Is Everything

  • A number floating on a page is just a number. A number at (x: 450, y: 120) next to a “Pressure Valve” label is actionable data.
• Tables Are “Impossible”

  • Engineering drawings are notorious for nested headers, merged cells, and non-standard layouts. Traditional CSV or Markdown exports flatten these into hallucinated columns and broken schemas.
• Overlapping Metadata

  • Annotations often sit directly on top of geometry. Standard OCR loses the relationship between text, symbols, and the components they describe.

Our Approach: Multi-Pass High-Fidelity Reconstruction

At Unstructured, technical drawings are processed through a specialized three-pass pipeline designed to turn raw geometry into machine-readable intelligence.

1. High-Resolution Element Identification

We don’t treat the page as a single image. Our high-resolution partitioner isolates every text string, numeric value, and symbol while preserving exact coordinate metadata. This guarantees that spatial relationships between labels, components, and values are never lost.

2. Multimodal Enrichment (The Semantic Glue)

Next, we layer in image-based enrichment using state-of-the-art vision reasoning models. This step adds semantic understanding: how components connect, what annotations refer to, and how visual elements relate to one another. Raw coordinates become meaningful technical facts.

3. Agentic Table Parsing (The Nested Breakthrough)

For complex engineering tables, we use Agentic Table Parsing. Instead of forcing tables into Markdown or CSV, we output them as HTML, which reliably preserves merged cells, nested headers, and non-standard hierarchies—critical for Bills of Materials (BOMs) and specification tables.

Optimizing Extraction: Auto vs. Hi-Res

You don’t need to build custom classifiers or routing logic.

• The “Auto” Pilot

  • Set your partitioner to strategy="auto", and Unstructured will automatically detect technical schematics and route them through the enhanced high-fidelity pipeline.
• The Power-User Configuration

  • For extremely dense or critical drawings, explicitly use strategy="hi_res" and enable Image Description Enrichment and Agentic Table Parsing to guarantee maximum structural capture.

What You Get Out the Other End

The output is normalized, queryable JSON that you can search, filter, and analyze engineering data programmatically. This includes:

• Plain-language descriptions for semantic search
• Structured text with precise positional metadata
• Table outputs in HTML that preserve nested structure

What This Unlocks for Your AI Roadmap

• Fact-Grounded RAG: Your agents can answer precise technical questions grounded in extracted facts and point directly to the exact location on a blueprint.
• Scalable Analytics: Extract structured data at scale to build time-series datasets or aggregate part specifications across thousands of historical drawings.
• High-Quality Fine-Tuning: Generate domain-specific instruction datasets with precise technical language and exact positional grounding.

Getting Started

Support for technical drawings is live in production.

• Use partitioner=auto to let the system detect and route drawings automatically
• Use partitioner=hi_res when you need guaranteed deep extraction for complex schematics

If technical drawings have been the weak link in your AI pipeline, this closes the gap.

Want to see it working end-to-end? Sign up for Unstructured and try one of your hardest schematics today.