The moment it happens
Sort support requests by topic and urgency
Each support form submission gets a category and urgency, and urgent ones reach you in Slack.
- Runs
- The moment it happens
- Apps
- Typeform
- AI
- Yes, uses the built-in AI allowance
- Access
- webhooks:write
Send this to your assistant
Create a ClickRuntime automation: when my 'Support' Typeform gets a response, sort it by category (billing, bug, account, other) and urgency, and post the urgent ones to #support in Slack.
First time? Adding ClickRuntime to your assistant takes about two minutes.
Why use it
Why teams use this automation
A support form that dumps everything into one inbox leaves the team to sort it. Billing questions, bug reports, feature requests and password problems arrive mixed together, and a person has to read each before deciding who handles it and how fast. Urgent outages are read last when they arrive late in the day, and nobody can say which topics generate the most requests.
Each time someone submits your Typeform, an AI step reads the request and assigns a topic and an urgency level. The flow keeps a running count per category and posts urgent requests to your team channel straight away, so the right person sees the right request.
It suits small support teams that do not yet have a full help desk. Counts build up in the background to show where requests come from.
What you get out of it
Urgent requests get seen first
A customer reporting that they cannot log in is flagged as urgent and posted straight away. The team responds to the serious cases while routine ones wait in the queue.
Requests reach the right person
Topic labels let billing questions go to finance and bugs go to engineering. Fewer handoffs and forwarded emails mean customers get answers from the right person sooner. Handoffs and forwarded emails drop quickly across the team.
Counts show where the pain is
The running totals per category reveal that a third of requests are about one feature. That is a clear argument for fixing the product or writing a help article. Planning conversations get real numbers to work with.
Consistent labels
A tired person will label requests differently from one day to the next. The AI applies the same categories every time, which makes the counts comparable week to week. Weekly comparisons become much more trustworthy than before.
Helps small teams scale
A team of two can handle growing volume without hiring just to sort mail. Triage happens instantly and the humans spend their time answering. Sorting never becomes the bottleneck on a small team.
How it works
What happens, step by step
When a support request comes in through your form, ClickRuntime picks it up straight away.
AI reads it and decides the topic, such as billing or login, and how urgent it is.
It keeps a running count of requests per topic.
Urgent requests are posted to your #support Slack channel with the sender's email and the issue, so someone can jump on them.
Before it runs live
- Connect Typeform
- Paste your automation's link into the other app's webhook settings
What you get
Build your own
Need it to work differently?
Your own rules
Conditions, thresholds, filters and exceptions, written the way you'd explain them to a colleague.
Any number of apps
One automation can read from one app and update several others. Over 248 apps are ready, and anything with an API works too.
AI where it helps
Summaries, classification, extraction and drafts use ClickRuntime's built-in AI, with no extra key.
On a schedule or on each event
Runs as often as every 5 minutes, or the moment an app sends an event, and keeps track of what it already handled.
Changes you can ask for
- Act only on the items that match your own rules, and skip the rest
- Change what the AI looks for, or how it writes the result
- Add another app, for example also send it to Slack
- Combine it with other Typeform steps into one automation
Under the hood
The code that runs this automation
from clickruntime import on_event, ai, notify
def answers(resp):
titles = {f["id"]: f["title"] for f in resp["definition"]["fields"]}
out = {}
for a in resp["answers"]:
value = a.get(a["type"])
if isinstance(value, dict):
value = value.get("label") or ", ".join(value.get("labels", []))
out[titles.get(a["field"]["id"], a["field"]["id"])] = value
return out
@on_event
def handle(event):
resp = (event.json or {}).get("form_response", {})
if resp.get("definition", {}).get("title") != "Support":
return {"posted": False}
text = "\n".join(f"{q}: {a}" for q, a in answers(resp).items())[:3000]
r = ai.generate(text, system="Categorise this support request.", max_output_tokens=60,
json_schema={"type": "object", "properties": {
"category": {"type": "string", "enum": ["billing", "bug", "account", "other"]},
"urgent": {"type": "boolean"}}}).json
if r["urgent"]:
notify.slack(f"Urgent {r['category']} request\n{text}", channel="#support")
return rFAQ
Common questions
Can I define my own categories?
What does urgent mean here?
Does it reply to the customer?
What permissions does it need?
Does it need AI?
How many runs does it use?
Can I change how it works?
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