Turning engineering drawings into a digital-twin asset database

We built a computer-vision pipeline that reads engineering drawings and spec tables, extracts the assets on them, and writes them into a structured database that feeds a digital twin — with low-confidence items routed to a human before anything is trusted.

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Domain · Telecom infrastructure Input · Engineering drawings & spec tables Output · Structured asset DB → digital twin

The challenge

The asset data was locked inside drawings

A digital twin is only as good as the asset data behind it — and here that data lived in stacks of engineering drawings and spec tables, not in a database. Building the twin by hand meant people reading each drawing and re-typing every asset: slow, expensive, and error-prone at exactly the scale where errors compound.

The goal was to get the structured asset database out of the drawings automatically — fast enough to be practical, accurate enough to trust as the foundation of a digital twin.

How it works

Vision does the reading. Structure and review keep it trustworthy.

Each stage has one job, and low-confidence results are escalated to a person rather than guessed.

Ingestion Drawings + tables

Takes in engineering drawings and their spec tables in the formats they already exist in — no re-drawing, no manual prep. Every source is tracked so extracted assets can be traced back to where they came from.

Detection & reading Computer vision + OCR

Computer vision detects the assets and symbols on the drawing; OCR reads the labels and spec-table values. This is the part that replaces hours of manual eyeballing — the model does the reading across the whole set.

Structuring Schema mapping

Detected elements are mapped into a defined asset schema — types, attributes, relationships — so the output is a clean, queryable database, not loose text. Each extracted field carries a confidence score.

Human review Human-in-the-loop

Anything below the confidence threshold is routed to a person to confirm or correct, instead of being trusted blindly. The model handles the volume; a human owns the edge cases — so the database earns the trust the twin depends on.

Digital-twin sync Structured output

Verified assets flow into the asset database that backs the digital twin — turning a pile of drawings into a live, queryable model of the real-world infrastructure.

Why it's trustworthy

Speed of AI, accuracy you can stake a twin on

What the system gives you

A structured, queryable asset database out of raw drawings.

A confidence score on every extraction, with a review queue for the rest.

Traceability — each asset links back to the source drawing.

A clean feed into the digital twin, kept current as drawings change.

How we ship CV to production

Human-in-the-loop where confidence is low — no silent guesses.

Evaluation on real drawings, not a demo sample.

Monitoring so accuracy is measured, not assumed.

Real engineering — AWS Solutions Architect certified.

Get started

Have documents or images hiding structured data?

Drawings, forms, inspection photos, spec sheets — if the data is trapped in images, we can get it out, structured and verified.

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