Find and score lookalike companies similar to your best clients

Finds companies that look like your best clients, scores them for outreach, and lists them in a spreadsheet.

How the work actually flows

It branches. Every path runs; runs once per lookalike company found.

Pattern: Parallel Split (2) ยท Multiple Instances without Synchronization (12)

flowchart TD trig(("you provide client domain list")):::human s0["find lookalike companies"]:::task s1[["enrich lookalike profiles"]]:::mi s2["calculate composite score"]:::task trig --> s0 s0 -->|"one per lookalike company found"| s1 s1 --> s2 gx{"+ is company a top lead"}:::gate s2 --> gx p00["record company details"]:::task gx -->|"log to spreadsheet"| p00 p10["send slack alert"]:::task gx -->|"flag in slack"| p10 p00 --> out p10 --> out out[/"ranked list of lookalike prospects"/]:::out pay{{"prioritized outreach without manual research"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepRuns once per itemA personEvery pathResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Email AutomationMessaging & NotificationsSpreadsheet & Database OpsHR & Recruiting
Connects
Google SheetsPredictLeadsSlack

The problem it solves

Finding new prospects who resemble your best customers usually means hours of manual research across news, hiring pages, and tech stacks. Without a consistent way to score them, your sales team ends up guessing which leads are worth chasing first.

Who it fits

Sales and business development teams looking to prioritize outbound prospecting.

How it works

  1. You start the process with a list of your best client domains
  2. The system finds similar companies for each client
  3. Each lookalike is enriched with recent news, hiring, and technology signals
  4. A composite score from 0 to 100 is calculated for outreach priority
  5. Top-scoring companies are logged to your spreadsheet and flagged in Slack
What you get

Lookalike prospects ranked by fit score

You get a ranked list of companies that look like your best clients, scored and flagged for your team to pursue.

What you get

A ranked spreadsheet of lookalike companies scored for outreach priority, with top leads flagged in Slack.

What you need

A Google Sheets account and a PredictLeads API subscription.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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