Building the quality the plan assumed

Human and AI oversight must be built into frontier systems from the beginning, not treated as a fallback when something goes wrong. There are always edge cases that have “it depends” answers – sometimes the answer is we do not know, but without any auditing or sampling, it’s impossible to know how well you did. AI and complex systems can drift, and accuracy is heavily dependent on the distribution of data, so continuous sampling and monitoring are critical components to these systems. Human users and reviewers in the accounting world are those who are accountable for outcomes, and they provide the signal of feedback to the models and weights. On our systems, we sample over 100,000 documents every month to ensure accuracy is a measurable metric, not a hope.

In our system, when a bookkeeper accepts, edits, or rejects AI suggestions, that data is fed into an evaluation loop too. The result is a continuously improving system that understands the unique financial context of each of the 700,000 SMBs in our system. Through these feedback loops and rigorous evaluations, outputs remain accurate and dependable over time.