AI-powered intake: turn vague requests into clean records
The slowest part of most request processes isn't the decision — it's the minutes (or hours) spent figuring out what was actually asked. Someone sends a vague ticket or a paragraph-long email, and the next person in line has to reconstruct the details before anything can happen.
AI-assisted intake closes that gap by extracting structure from natural language as the request is submitted.
What AI actually does on intake
Good AI on intake isn't a chatbot bolted onto a form. It's a layer that reads what the requester wrote and turns it into the fields approvers need.
- Summaries. A short, neutral recap of what's being asked and why — so approvers can understand a long request at a glance.
- Field extraction. Project codes, dates, amounts, vendor names, and other structured values pulled out of free text and written into the right fields.
- Priority hints. Cues like "by end of day" or "production is down" surface as suggested priority, so urgent work doesn't sit in a queue behind routine tasks.
- Missing-info prompts. If the requester forgot to attach a quote or skipped a required justification, the form asks for it before submission.
Why this matters downstream
Clean intake isn't just about the requester's experience. It changes what happens after submit.
- Approvers spend less time reading and more time deciding.
- Data in reports actually reflects reality, because categories and amounts are captured consistently.
- Routing rules fire correctly, because the fields they depend on are populated.
The human stays in charge
AI handles the busywork of parsing and organizing. Decisions — approve, reject, route, escalate — stay with the people accountable for them. The goal is to give those people a clearer signal, not to replace their judgment.
Try it
In Requset, AI summaries and field extraction are built into the intake step of every workflow. Browse the template gallery to see examples, or start free and run it on your own process.