CUSTOMER SUPPORT
Cross-Channel Duplicate Collapser with Postgres Identity Ledger
Resolves the same customer across email, chat, and SMS using a Postgres identity ledger, then merges their scattered Front threads into one even when the addresses differ.
How it runs
The automated pipeline, trigger to output.
- TriggerFront conversation created or updatedFront
- ActionResolve canonical customer ID in Postgres ledgerPostgres
- LogicCollect all open threads for that customer
- ActionMerge threads into primary conversationFront
- OutputWrite surviving thread ID back to PostgresPostgres
What it does
A customer who emails from work, chats from their phone, and texts support looks like three different people to Front. This workflow uses a Postgres identity ledger to map all of a person's handles to one canonical customer ID, then collapses every matching Front conversation into a single thread.
When to use it
Use it when duplicate threads come from the *same human across different channels or addresses*, not just repeated emails. Ideal if you already maintain a customer table in Postgres and simple sender-match dedup misses too many cases.
How it works
- 1A new or updated Front conversation triggers the flow.
- 2The flow extracts the sender handle (email, phone, or chat ID) and queries the Postgres ledger to resolve the canonical customer ID.
- 3A logic step gathers all Front conversation IDs previously linked to that customer ID.
- 4If multiple open threads resolve to one customer, Front merges them into the primary conversation.
- 5The ledger is updated with the surviving conversation ID, and the merge result is written back to Postgres for audit.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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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