What to expect from an AI workflow audit

A workflow audit is the cornerstone of how we work, and it is also where most engagements get the most value per dollar. Here's what one looks like from your side of the table.

Before the audit

After the discovery call, we send a short intake questionnaire — what your team does, what tools you're running, where the obvious pain points are. We pair that with whoever on your team owns the workflows we're going to look at and set up two or three short interviews. Total time on your side, including the questionnaire and interviews, is usually three to four hours.

During the audit

Our engineers spend a week or two — depending on the tier — sitting with the data. We watch how work moves through your team, where it gets stuck, which steps an AI workflow could realistically take on, and which steps need to stay with a human. We compare candidate platforms against your data sensitivity, your existing stack, and your budget.

What we deliver

• A written report — usually 15 to 40 pages depending on the audit tier — that your managing partner or owner can read, share with stakeholders, and act on without us in the room.
• A prioritized list of workflow automations with estimated time savings, platform recommendations, and security considerations called out.
• A fixed-fee implementation quote for the recommended scope. You know the number before you decide to proceed.
• A "do nothing" recommendation where it applies — sometimes the right answer is "your team is already running this efficiently; don't spend money."

How to get the most out of it

• Be honest about what is not working. The audit only finds what your team is willing to surface.
• Have the right people in the room. The lead engineer needs to talk to the people actually doing the work, not just the partner who manages them.
• Don't pre-commit to a platform. Let the audit recommend what fits.