CUSTOMER SUPPORT
Route translated tickets using a Postgres language-skill roster
Detects each new Front ticket's language, looks up which agents currently have that language skill in a Postgres roster.
How it runs
The automated pipeline, trigger to output.
- TriggerNew Front conversation receivedFront
- ActionDetect language via Hugging FaceHugging Face
- ActionTranslate body to English via Hugging FaceHugging Face
- ActionQuery Postgres for on-shift agents with that language skillPostgres
- LogicBranch on whether a matching agent is available
- OutputAssign to agent or tag for multilingual backlogFront
What it does
Instead of hardcoding language-to-inbox mappings, this workflow reads a live agent skill roster from Postgres. It detects the ticket language, queries which on-shift agents speak it, and assigns the conversation directly to a specific available agent rather than a generic queue. It also attaches an English translation so any reviewer can follow the thread.
When to use it
Your language coverage changes by shift and you want assignments to follow who is actually working right now. Use it when a static inbox-per-language setup leaves tickets stranded whenever a language specialist is off.
How it works
- 1A new Front conversation triggers the flow.
- 2A Hugging Face model detects the language code of the message body.
- 3The body is translated to English with a Hugging Face translation model.
- 4Postgres is queried for agents whose skill row matches the language and whose status is on-shift, ordered by lowest current load.
- 5A branch handles the empty case: if no matching agent is available, the ticket is tagged for the multilingual backlog instead.
- 6The conversation is assigned to the chosen agent and the translation is posted as a private comment.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 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.
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Run this workflow in your colony.
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