Building AI Quality Checks for Construction Billing: Lessons from Real Pay Application Errors
We build construction billing software, and after seeing the same pay app mistakes repeatedly, we started experimenting with AI-assisted quality checks. Here’s what surprised us…
Most people working on AI tools right now are building chatbots, coding
assistants, or trying to automate customer support.
We ended up using AI for something much less glamorous:
Trying to catch construction billing mistakes before a subcontractor
submits a pay application and waits 30 days to find out something
doesn't tie out.
Not exactly Silicon Valley material.
But if you've spent any time around construction billing, you know this
stuff matters. A missed retainage calculation or a continuation sheet
mismatch can delay payments, trigger back-and-forth emails, or force
someone to rebuild a spreadsheet at 8 PM on a Friday.
We build software for construction pay applications, and after seeing
the same mistakes repeat over and over, we started asking:
Could AI review a pay app the way an experienced billing person would?
Not write it. Review it.
That distinction matters.
The Problem: Most Errors Aren't Obvious
The funny thing about construction billing errors is that they're rarely
dramatic.
Nobody submits:
Current payment due: $500,000
Previous billing: BANANA
The mistakes are subtle.
- Retainage doesn't match historical calculations
- Stored materials are billed but unsupported
- Approved change orders weren't included
- Continuation sheet totals don't roll correctly
- Prior application amounts don't align
- Schedule of Values values shifted over time
- Attachments are missing
- Billing percentages exceed expected ranges
Can software catch them earlier?
We Didn't Want AI to Replace Rules
Construction billing has lots of deterministic logic.
If approved change orders exist but contract values don't update, that
isn't AI --- that's math.
We separated checks into:
Rule-based checks
- Missing project information
- Amount mismatches
- Retainage inconsistencies
- Missing required documents
- Invalid percentages
- Change order math
AI-assisted checks
- Does this billing pattern look unusual?
- Is supporting documentation probably insufficient?
- Does this resemble submissions that get kicked back?
- Is there risk despite passing calculations?
The First Versions Were Annoying
Users hated outputs like:
FACT:
Stored materials exist.
Users preferred:
Issue:
Stored materials billed without supporting documentation.
Suggestion:
Attach invoices or supplier documentation.
Actionable beats informative.
Scoring Was Harder Than Detection
Not all issues carry equal risk:
- Minor issue: -2
- Moderate issue: -7
- High impact issue: -20
Construction workflows differ, so tuning never stops.
Legacy Systems Add Weird Constraints
Real systems often still use:
- Older PHP
- Older MySQL
- Limited JSON support
Building AI features without breaking existing workflows is harder than
it sounds.
AI Needs Context
Numbers alone create false alarms.
Historical billing, retainage overrides, stored materials, and change
orders all matter.
Context matters more than intelligence.
The Goal Isn't Perfection
The goal is reducing avoidable delays and catching obvious problems
earlier.
If software prevents one rejected pay app or one payment delay, it
created value.
Final Thought
Some of the most useful AI applications aren't flashy.
Sometimes they're reviewing retainage calculations before a pay
application goes out the door.
That might matter more.
Where have you seen AI help in operational workflows --- and where do
traditional rules still outperform it?
We build software that helps contractors streamline construction billing workflows, pay applications, and related processes.
Learn more:
https://payapppro.com/
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