CUSTOMER SUPPORT
Glossary Term Consistency Enforcer for Localized Macros
When a localized macro is submitted, checks every approved glossary term against the translation, round-trips disputed phrases.
How it runs
The automated pipeline, trigger to output.
- TriggerLocalized macro submitted in AirtableAirtable
- ActionRead approved glossary for target languageAirtable
- ActionCheck terms + back-translate deviationsOpenAI
- LogicBranch: approve clean vs reject violations
- ActionUpdate row with per-term correctionsAirtable
- OutputNotify submitter of fixes in SlackSlack
What it does
Enforces your approved bilingual glossary across localized macros. It verifies that protected terms (product names, legal phrasing, brand tone words) are translated exactly as the glossary mandates, and round-trips any phrase that deviates to confirm whether meaning actually changed.
When to use it
Use it when consistency of specific terms matters more than free-form fluency — regulated industries, brand-name handling, or contractual language where one wrong translated term creates risk. It makes the glossary the source of truth, not the translator's judgment.
How it works
- 1A localized macro is submitted as a row in Airtable.
- 2The approved glossary for the target language is read from Airtable.
- 3OpenAI checks the macro against each glossary term and flags any term rendered differently than required.
- 4For each flagged term, OpenAI back-translates the surrounding phrase to confirm whether meaning shifted.
- 5A logic branch decides: clean macros are marked "Approved", violations are marked "Rejected".
- 6The Airtable row is updated with per-term corrections, and a Slack note tells the submitter exactly which terms to fix.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect OpenAIModels, embeddings, files.
- 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
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.
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.
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.
Article Volume-Rebound Early Warning (Datadog)
Streams support ticket-tag events into Datadog, watches for topics whose volume is reaccelerating against a decaying article.
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.