CRM

Compute a Stakeholder Influence Graph and Store It in Postgres

Aggregates every email thread on a deal, builds a weighted who-replies-to-whom influence graph, scores each stakeholder's centrality.

CategoryCRM
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule per open deal
  • ActionRetrieve all deal threadsGmailGmail
  • LogicBuild weighted reply-direction edge list
  • ActionLabel nodes and resolve alias addressesOpenAI
  • LogicCompute centrality and influence scores
  • OutputUpsert committee graph snapshot to PostgresPostgreSQLPostgres

What it does

Produces a quantitative influence graph of a deal's buying committee. Instead of just labeling roles, it measures interaction volume and reply direction across all threads to rank who actually drives the conversation, then stores the structured graph for downstream reporting.

When to use it

Reach for this when you want hard data behind committee dynamics across many deals, feed a deal-health dashboard, or compare influence patterns between won and lost opportunities.

How it works

  1. 1A weekly schedule kicks off the run for each open deal.
  2. 2Gmail retrieves all threads associated with the deal and its participants.
  3. 3The flow tallies messages, replies, and addressing direction to build a weighted edge list between participants.
  4. 4OpenAI labels each node with a role and resolves duplicate or alias addresses to single people.
  5. 5Centrality and influence scores are computed per stakeholder from the edge weights.
  6. 6The ranked nodes and edges are upserted into Postgres committee tables, replacing the deal's prior snapshot.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  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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