Illustrative scenario
A busy operations teamOperations20–50 people
This isn’t a real customer story — Planoda is pre-launch. It’s an honest walkthrough of how the features we’ve already shipped map onto a team like this one.
Inbound requests for a team like this arrive from Slack, email, and forms with no consistent routing. Someone spends part of every day reading, prioritizing, and forwarding tickets by hand.
Intake unifies those channels into one queue, and AI triage reads each request to suggest a priority and route it to the right team — turning a raw message into a structured, assignable issue automatically.
Manual sorting moves off people's plates, and because triage is a suggestion the team stays in control of the judgment calls. The audit log records what was auto-routed and why, so nothing is a black box.
Inbound work for an operations team arrives through every door at once — Slack threads, forwarded emails, half-filled forms — and in no consistent shape. Triaging it is unavoidable but unrewarding: someone has to read each request, judge its urgency, catch duplicates, and hand it to the right owner. That someone is usually a senior person whose hour is better spent elsewhere, and the work resists batching because every item looks a little different.
Intake collapses the channels into a single queue, then AI triage does the first pass: it reads each request, proposes a priority, attaches it to the likely project, flags probable duplicates, and suggests a route — all before a human opens it. The key design choice is that triage proposes rather than decides. A person stays in the loop on the judgment calls, and every automated route writes an audit row, so a mis-route is explainable and reversible instead of mysterious.
The repetitive majority of intake moves itself, and human attention concentrates on the genuinely ambiguous cases that actually need a person. Because routing is suggested and logged rather than silent, the team keeps both control and an answer to 'why did this end up here?' — turning a daily hour of manual sorting into a reviewable, mostly-hands-off flow.
Illustrative, directional figures — not measured customer results. They show the mechanism, not a promised number.
Illustrative — composite of real workflows, not a customer testimonial
“Most of the queue now sorts itself before I look at it, and when I do look, it's the handful of requests that genuinely needed a human in the first place.”
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