SUMMARIZATION

Weekly export of Front decisions to a searchable Postgres registry

Each week, an AI processes all resolved Front threads, extracts structured decisions, and upserts them into a Postgres table that powers a queryable.

CategorySummarization
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionFetch Front threads resolved this weekFront
  • ActionExtract normalized decision recordsOpenAI
  • LogicDeduplicate against stable decision key
  • OutputUpsert decisions into Postgres registryPostgreSQLPostgres

What it does

Builds and maintains a durable, queryable decisions registry in Postgres. On a weekly schedule, the workflow gathers the week's resolved Front conversations, extracts each decision into a normalized record (subject, decision, owner, date, source thread), and upserts it into a Postgres table — deduplicating against decisions already captured so reruns stay idempotent.

When to use it

Use it when you want decision data you can actually query with SQL — joins against accounts, full-text search, or feeding a downstream dashboard — rather than scattered docs. Ideal for ops and data teams that already centralize on Postgres and need an auditable, long-lived decision history.

How it works

  1. 1A weekly schedule trigger fires.
  2. 2The workflow pulls all Front conversations resolved in the past week and their messages.
  3. 3An OpenAI model extracts a normalized list of structured decision records from each thread.
  4. 4A logic step deduplicates against a stable decision key so existing rows are updated, not duplicated.
  5. 5Records are upserted into the Postgres decisions table, ready for SQL search and dashboards.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  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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