MasonThe market for AI plan review software is growing faster than the standards for evaluating it. Every tool promises speed. Every tool claims accuracy. Almost none of them explain what happens when they are wrong.
Before you trust any AI system with your professional output, there are five questions worth asking. Not every vendor will answer all of them. How they respond will tell you as much as the answers themselves.
Does the tool show its work?
A plan review AI that gives you a conclusion without a code citation is not a review tool. It is a guess. Before you accept any flag, you need to be able to read the same section the tool is drawing from and confirm that the reasoning holds. If the tool cannot tell you which section of which code book it is referencing, you are not doing AI-assisted plan review. You are outsourcing your professional judgment to a black box.
Ask any vendor: where does the code reference appear in the output? Can the reviewer read it directly? Can they click through to the full text?
What happens when the tool is uncertain?
Code is not always clear. Some provisions conflict. Some drawings fall into gray areas where reasonable professionals disagree. A good AI plan review system should be able to surface those gray areas rather than forcing a definitive answer.
Ask: does the tool distinguish between clear violations and judgment calls? Does it tell the reviewer when the evidence is thin? Or does it present every output with the same confidence?
Can a reviewer override the system without penalty?
The reviewer is the professional. The AI is the tool. If a tool makes it difficult, awkward, or technically impossible for a reviewer to override a flag, the design is wrong. Professional judgment requires the freedom to disagree with the AI and document the reasoning for doing so.
Ask: what does the override workflow look like? Does the system record overrides? Can the reviewer add a note explaining their reasoning?
How is the tool trained and updated?
Building codes change. A tool trained on 2020 code editions is not reliable for 2025 reviews. The same applies to jurisdiction-specific amendments, which vary significantly across California counties, let alone across states.
Ask: how often is the model updated? Who reviews the updates before they go into production? Is there a way for users to flag code errors they find in the output?
What is the liability model?
This is the question most vendors are not prepared to answer clearly. When an AI tool misses a code violation and a building inspector catches it on the back end, who is responsible? The professional whose name is on the letter. Always.
A tool that markets itself as reducing liability is making a claim it cannot support. The liability belongs to the license holder. What a tool can do is reduce the probability of missing something, and document the process well enough to show due diligence.
Ask: does the vendor make any claims about liability? If they do, ask them to put it in writing. The ones who will not are being honest with you.
What to watch for
The red flags in this space are consistent. Tools that present conclusions without reasoning. Tools that promise to replace professional review rather than support it. Tools that make the override process difficult. Tools that update their models without notifying users.
The right tool treats the reviewer as the decision-maker and gives them better information. It does not try to be the decision-maker.
If you want to see how Mason answers these questions in practice, the product page walks through the interface in detail. Every flag includes a code citation. Every citation links to the full text. Reviewers control every output before it becomes a comment.
Trust the tool you would stake your professional license on. If you would not stake your license on it, it is not ready.