The Process Problem
A growing services team receives a new client through an intake form, then manually creates a CRM record, kicks off a project in their project-management tool, sends a welcome and next-steps email, assigns internal setup tasks, and chases down any missing paperwork — the same multi-step checklist, repeated by hand for every single new client. Nothing about this process is complicated, which is exactly the problem: it's a dozen small, correct steps that are easy to get right once and easy to let slip when someone's busy, on vacation, or juggling five onboardings at once.
Automation Approach
The intake form submission triggers the workflow directly — no one has to remember to kick it off. The workflow creates the CRM contact and the project record in the same step, so the two systems never drift out of sync. Any document uploaded with the form (a signed agreement, an ID, a questionnaire) gets read by an AI step that extracts the key fields — name, dates, service tier, any custom terms — and compares them against what the client typed into the form itself. A mismatch or a low-confidence read gets flagged for a human to check instead of being filed silently. Once the records are created and documents are verified, the workflow sends the onboarding email sequence and creates the internal setup tasks assigned to the right team member based on service type.
Why This Approach
Document verification is the one step that genuinely needs AI rather than a plain rule — a simple automation can move a file from A to B, but it can't read a signed agreement and tell you whether the dates match what the client claimed on the form. Everything else in this workflow (creating records, sending emails, assigning tasks) is deliberately kept as straightforward rule-based automation, because adding AI to a step that doesn't need judgment just adds cost and another thing that can misfire. The result is one AI step doing real work, surrounded by cheaper, more predictable automation doing the rest.