CUSTOMER SUPPORT
Daily digest of inbound ticket language mix and routing accuracy
On a daily schedule, reads yesterday's tickets from Postgres, classifies each body's language with Hugging Face, compares it against where the ticket was actually routed.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionRead yesterday's tickets from PostgresPostgres
- ActionClassify each ticket language via Hugging FaceHugging Face
- LogicAggregate language mix and flag misroutes
- OutputPost language-mix digest to SlackSlack
What it does
This is a reporting workflow, not a live router. Once a day it pulls the prior day's tickets from Postgres, runs each body through a Hugging Face language classifier, and cross-checks the detected language against the queue the ticket was assigned to. It summarizes the inbound language mix and flags conversations that landed in the wrong language queue, then posts the digest to a Slack channel.
When to use it
You already route multilingual tickets and want to know whether the routing is actually correct and how your language volume is trending. Use it to spot a growing language you do not staff, or a queue quietly accumulating misrouted tickets.
How it works
- 1A daily schedule triggers the flow.
- 2Postgres returns yesterday's tickets with their body text and assigned queue.
- 3Each body is classified by a Hugging Face language model.
- 4A logic step aggregates counts per language and compares detected language to assigned queue to flag misroutes.
- 5A formatted digest with the language mix and a misroute list is posted to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Customer Support workflows
Send a tailored Loom onboarding sequence on Front first-reply
When a new customer's first email lands in Front, this picks the Loom onboarding walkthroughs matching their plan and use case, builds a friendly sequenced reply.
Suggest the right Loom video by classifying Intercom message intent
Reads each new inbound Intercom conversation, classifies what the customer is trying to do, and surfaces the best-matching Loom walkthrough to the agent as an internal note.
Draft personalized fix-live replies for support to review
When a Sentry issue resolves, an agent reads each linked ticket's full thread and drafts a tailored 'your fix is live' reply per requester.
Close the loop with requesters when a Linear bug moves to Done
When a Linear issue created from a support escalation moves to Done after deploy, look up the originating Zendesk tickets and notify each requester that their reported bug is…
Reopen and notify Front conversations when their bug fix deploys
When a deploy resolves a Sentry issue, find the snoozed or closed Front conversations linked to it, reopen them, and send the customer a reply that the fix is now live.
Tell Intercom users their reported bug shipped after a Vercel deploy
On a successful Vercel production deployment, match the release's resolved Sentry issues to Intercom conversations and message each affected user that their reported issue is…
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.