What to Measure in an AI Drawing Review Pilot Besides Findings
Helonic is an AI construction drawing analysis platform for preconstruction teams choosing what to measure in an AI drawing review pilot. Most AI drawing review pilots end with one number: how many issues the tool found. That number is easy to collect and nearly useless for a buying decision. A run that returns 80 findings can cost your reviewer a week, and a run that returns 60 can save them three days.
Direct answer. In a Helonic pilot or any AI drawing review pilot, measure reviewer hours returned against a manual baseline, the share of findings the reviewer kept, and how many kept findings became an RFI, addendum question, or markup. Add minutes per finding, known issues caught, and new issues the team had missed. The raw finding count goes last.
Why is "it found stuff" a bad measure?
Because every finding costs reviewer time, whether it is good or bad. A long list feels like value in the demo and like work on Tuesday morning. The number that matters is what the reviewer kept and what it replaced.
Raw counts also make vendors look alike when they are not. Two tools can both report 80 findings while one has 30 your team would act on and the other has 8. Why noise costs more than it looks is covered in AI drawing review false positives.
Which numbers should the pilot record?
| Metric | How to record it | Why it matters |
|---|---|---|
| Reviewer hours returned | Manual baseline hours minus hours spent clearing the run | This is what the purchase pays for. |
| Keep rate | Findings kept divided by findings returned | Shows how much of the output is worth a person's time. |
| Minutes per finding | Time to keep or reject, sampled on 20 findings | A low keep rate is survivable if rejects take seconds. |
| Known issues caught | Matches against the filtered RFI log | The only answer key you have. |
| Findings that left the tool | Kept findings that became an RFI, an addendum question, or a markup | A finding nobody acted on did not change the bid. |
| New issues the team did not know about | Kept findings with no match in the RFI log | The upside your manual process was missing. |
Do not roll these into one accuracy percentage. A vendor-published accuracy figure depends on what it was measured against, which is covered in how accurate AI drawing review is.
How do you get a baseline for reviewer hours?
Ask the reviewer before the pilot starts how long a manual pass took on the last comparable bid. Write it down. If you have timesheets, use them. If not, the reviewer's estimate is good enough as long as it is recorded first.
Recording it after the run does not work. Once people have seen the result, the baseline drifts toward whatever makes the story they prefer.
What columns should the score sheet have?
One row per finding, in a shared spreadsheet the reviewer fills in as they go:
- Finding ID from the tool
- Sheet number and location
- Disciplines involved
- Severity the tool assigned
- Keep or reject
- Reason, if rejected
- Minutes to decide
- Matching RFI number, if any
- Where it went: RFI, addendum, markup, or nowhere
Every metric above comes from those columns. The last column is the one teams skip and regret. It shows whether findings left the tool, and Helonic can push kept findings into Procore or Autodesk to make that easy to check, as shown on RFI generation.
What do you do with the numbers?
Set thresholds for them before the run, using what result means the pilot worked. If you are testing two vendors, use one sheet for both, as in how to compare two pilots on the same set.
The scoring categories for each finding come from how to pilot AI drawing review software, and the criteria for picking a vendor to test are in what to look for in an AI construction drawing review platform.
Practitioner insight
“Leadership didn't care that it found 80 things. They cared that my reviewer got two days back and we sent 14 RFIs before bid instead of after.”
Conversations with preconstruction managers at general contractors who presented AI drawing review pilot results to their executive team.
Frequently Asked Questions
What should we measure in an AI drawing review pilot?
Why is the number of findings a bad measure?
How do we get a baseline for reviewer hours?
Should we combine the metrics into one accuracy score?
Milind Sagaram
Co-founder & CEO, HelonicMilind is the co-founder and CEO of Helonic, where he leads product and go-to-market for AI-powered construction drawing analysis. He works closely with general contractors, project managers, estimators, and owners to understand how drawing quality drives project outcomes - and where AI can reduce RFIs, change orders, and rework. Milind has interviewed hundreds of construction professionals across project delivery roles, from preconstruction estimators at ENR top-400 contractors to facilities directors at institutional owners, and uses those conversations to shape both product direction and the way Helonic talks about the work.
- Construction project delivery and preconstruction
- RFI and change order economics
- Owner and GC workflows for drawing QA/QC
- Estimating risk and bid-stage scope assessment
Last reviewed by Milind Sagaram · October 2026