CRM

Maintain a Postgres Signature-Drift Audit Log and Snapshot Title History

Records every parsed email signature into a Postgres history table, computes diffs against the last seen title per contact, and stores an auditable timeline of role changes.

CategoryCRM
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled batch run of new emails since last executionOutlook
  • ActionParse each signature into name, email, and titleOpenAI
  • ActionQuery Postgres for latest stored title per contactPostgreSQLPostgres
  • LogicKeep only changed or new contacts
  • ActionInsert versioned title-history row with diff and sourcePostgreSQLPostgres
  • OutputReport count of drift events capturedPostgreSQLPostgres

What it does

This workflow turns signature drift into structured history. For each inbound email it parses the signature, looks up the contact's most recent stored title in Postgres, and when the title has changed it inserts a new versioned row capturing the before and after with a timestamp and source message ID.

When to use it

Use it when you need an audit trail rather than just a current value, for example to power churn analysis, to prove when a buyer changed roles, or to feed an org-mapping dashboard. It is the warehouse layer that sits underneath the CRM-writeback workflows.

How it works

  1. 1A scheduled run pulls the batch of new emails received since the last execution from Outlook.
  2. 2OpenAI parses each signature into a structured name, email, and title record.
  3. 3The flow queries Postgres for the latest stored title for each contact.
  4. 4A logic step keeps only contacts whose title changed or who are new.
  5. 5Postgres inserts a versioned history row with old title, new title, timestamp, and message ID.
  6. 6A run summary reports how many drift events were captured.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect OutlookMail, calendar, contacts.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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