Extraction & Structuring

Extract Structured Data from Technical PDFs and Spec Sheets

Upload a PDF or DOCX and get back structured, organized data — every property, value, unit, and standard reference identified and ready to use. No manual cleanup, no copy-paste errors.

Position-aware extraction

Reads table structure from coordinates, not just text. Understands that “45 cSt” in the third column is a viscosity value, not random text next to a heading.

Domain detection

Auto-classifies your document’s industry — coatings, hydraulics, food processing, or any of 13 verticals — so property names and categories are correct for your domain.

Structure-only mode

Select zero target languages to run extraction, structuring, and auditing only — no translation. Export structured data as JSON, Excel, PDF, or DOCX.

Diagram preservation

Embedded images — performance charts, dimensional drawings, product photos — are extracted from PDF and DOCX files and matched to their associated sections.

Raw PDF text vs structured extraction:

What you get from a PDF

Kinematic viscosity 45 cSt
at 40°C ASTM D445
Flash point 210 °C
ISO 2592 (COC)
Density 0.87 kg/L
at 15°C ASTM D4052

What SpecMake extracts

Property: Kinematic viscosity
Value: 45 cSt at 40°C
Standard: ASTM D445

Property: Flash point
Value: 210 °C
Standard: ISO 2592 (COC)

Property: Density
Value: 0.87 kg/L at 15°C
Standard: ASTM D4052

How extraction works

SpecMake doesn't just read the text from your document — it reads the document the way an engineer would. The system uses a position-aware text layer that reconstructs table rows from Y-coordinates and identifies columns from X-axis gaps. This means it understands that “45 cSt” in the third column of a table is a kinematic viscosity value, not random text floating next to a heading.

For PDFs, this positional analysis works alongside the visual document content. The system sees both the rendered page and the underlying text structure, cross-referencing them to extract values accurately — even from complex multi-column tables, nested specifications, and documents with mixed layouts.

The output is structured JSON. Every extracted property comes with its name, value, unit, and any associated test standard or condition. This structured format is what makes everything downstream possible — domain-aware translation, the quality audit, and clean document generation all work from this structured data.

From spec sheets to product databases

Structured extraction isn't just a step toward translation — it's valuable on its own. Companies managing large product portfolios often need to get specification data out of PDFs and into systems that can actually work with it: PIM platforms, e-commerce product databases, comparison tools, or internal engineering databases.

The JSON and Excel export formats are designed for this. Download your structured data and import it directly — no manual transcription, no copy-paste errors, no paying someone to key in values from a 30-page PDF.

With the EU's Digital Product Passport requirements approaching, having product specifications in structured, machine-readable formats is becoming a compliance requirement, not just a convenience. Extraction is the foundation for getting documentation DPP-ready — you can't structure what you haven't extracted.

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