Mason AI

Building AI for a highly specialized professional workflow has taught us something important: automation is not always about replacing the person doing the work.

Plan review is a good example.

Professional reviewers are responsible for evaluating plans, interpreting applicable requirements, and deciding what belongs in the final review. Simply generating a list of AI findings doesn't remove that responsibility. In fact, if the output cannot be verified, it can create another layer of work.

That is the problem we're working on with Mason AI.

Instead of treating AI as the final decision-maker, we're building it to support the first pass of the review process. Mason analyzes plan sets, identifies potential code issues within its supported scope, connects findings to relevant code references, and helps prepare draft review comments.

The reviewer remains responsible for the final output.

We've found this framing especially important when building for a niche professional audience. Our users don't necessarily want another dashboard or a tool that asks them to completely change how they work. They want something that can fit into an existing process and help them spend less time on repetitive tasks.

Another lesson is that trust is a product feature.

For AI to be useful in a professional workflow, users need to be able to inspect the output, verify the supporting information, and disagree with the system when their expertise or project context leads them to a different conclusion.

That's why we're focused on making AI a second pair of eyes rather than presenting it as a replacement for professional judgment.

We're continuing to learn from private plan reviewers and plan review teams about where AI can genuinely save time—and where the human reviewer should always remain at the center of the decision.

Learn more about Mason AI: Mason AI