CUSTOMER SUPPORT
Remember each Front contact's language preference and pre-translate replies
On the first non-English conversation from a contact, detects and stores their preferred language in Postgres.
How it runs
The automated pipeline, trigger to output.
- TriggerNew Front conversation from a contactFront
- ActionLook up stored language preference in PostgresPostgres
- LogicBranch: preference exists vs first contact
- ActionDetect language with OpenAI and store preferenceOpenAI
- ActionApply language tag to conversation in FrontFront
- OutputPost preferred-language internal noteFront
What it does
Detecting language on every message wastes calls and occasionally flips on short or mixed-language replies. This template detects a contact's language once, stores it as a durable preference in Postgres, and reuses it on every future conversation, tagging the conversation and posting the saved preference as an internal note so the agent replies in the right language from the first touch.
When to use it
Use it when the same customers contact you repeatedly and you want consistent language handling across their conversation history, not a fresh guess each time. It reduces misclassification on follow-ups and gives agents an at-a-glance language cue.
How it works
- 1A new Front conversation triggers the flow and the contact's ID is read.
- 2Postgres is queried for a stored language preference for that contact.
- 3A branch checks whether a preference already exists.
- 4If none exists, OpenAI detects the language and the preference is written to Postgres.
- 5Front applies a language tag to the conversation using the known or newly detected language.
- 6Front posts an internal note stating the contact's preferred language for the agent.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect OpenAIModels, embeddings, files.
- 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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