How accurate is BuildVision's AI?

BuildVision's overall accuracy is 89% across 100,000+ AI executions on 12 procurement workflows, measured on real construction documents. Document classification runs at 97%+, equipment extraction at 95%+, and component spec parsing at 99%+.

What does BuildVision measure in its benchmark?

BuildVision measures accuracy across 12 distinct procurement workflows including document classification, equipment extraction, component spec parsing, equipment quantity detection, table alternates, and complex mechanical schedule parsing. All measurements use real construction project data verified against human ground truth.

How often is the BuildVision benchmark updated?

The benchmark is updated quarterly. When models improve or new workflows are added, the numbers change. If accuracy drops on something, that shows up too.

What AI tasks does BuildVision consider solved?

Component spec parsing at 99%+, document classification at 97%+, equipment extraction at 95%+, and equipment quantity detection at 90.5% are considered effectively solved. Complex mechanical schedules at 81% and table alternates at 83.1% are still improving.

Published Accuracy

Name: BuildVision AI procurement accuracy benchmark
Creator: BuildVision

Everyone in construction tech claims AI. Almost nobody shows the numbers.

We publish ours because you should be able to check.

The Problem with "AI"

"40% efficiency gains! 10x faster workflows! Revolutionary AI!"

— Every pitch deck, ever

"But... can I see the math?"

— You, hopefully

You wouldn't quote a $50M mechanical package without reading the spec. You'd check every section, flag the alternates, and know exactly what you're pricing.

Why would software be any different?

Our Numbers

100K+

AI executions

distinct workflows

89%

overall accuracy

What's Effectively Solved

Component Spec Parsing 99%+

Document Classification 97%+

Equipment Extraction 95%+

Equipment Quantity 90.5%

What We're Still Improving

Table Alternates 83.1%

Complex Mech Schedules 81%

The gap between these and the solved tasks tells you where a human still needs to check.

What These Tasks Mean

Document Classification

Is this a spec, drawing, schedule, or addendum? Misclassify a document and everything downstream inherits the error.

Equipment Extraction

What MEP equipment is specified? Miss a chiller buried in an addendum and you miss the project entirely.

Component Specs

Capacities, efficiencies, voltages, RPMs. These determine whether equipment actually meets design intent.

Table Alternates

What substitutions are acceptable? Knowing your options early changes how you price the job and which lines you push.

Each task sounds straightforward. In practice, construction documents are messy. Specs contradict drawings. Addenda override base documents. Equipment schedules use different naming conventions from page to page.

That's why we measure on real project data.

Why We're Publishing This

Receipts > promises

We say equipment extraction runs at 95%. Here are the executions.

We say we've processed tens of thousands of specs. Here's the data.

You can check.

Updated quarterly. Last updated: Q1 2026.

When models improve or we add new workflows, the numbers here change. If accuracy drops on something, that shows up too.

See for yourself

See It On Your Documents

Send us a bid package. See the extractions. Judge the accuracy yourself.

Get Started

Data from production workloads over the past quarter. Accuracy measured against human-verified ground truth. Some things work well. Some don't yet. That's the point of publishing it.