How to Build an AI Lead Scoring System in GoHighLevel
Most GoHighLevel users treat every lead the same. The ones who win treat leads based on data — routing the hottest leads to the best reps immediately and nurturing the rest automatically. This guide shows you how to build a complete AI lead scoring system inside GoHighLevel using Make.com.
The Logic Before the Build
When a new lead enters GoHighLevel — from a form, an ad, a chat widget, or a manual import — the scoring system activates automatically. Make.com retrieves the lead’s data and sends it to an enrichment service (Apollo.io) to add company size, industry, and job title data that was not in the form. The enriched data is passed to Claude with your scoring rubric. Claude returns a score (0-100) and a qualification tier (A, B, C, or D). Make.com writes the score and tier back to the GoHighLevel contact record as custom fields. GoHighLevel automations then route the lead based on tier: Tier A gets an immediate notification to your best rep; Tier B enters the standard nurture sequence; Tier C enters a long-term drip; Tier D is tagged as unqualified.
Preparing GHL Before Make.com
Create custom fields for scoring data
In GoHighLevel, go to Settings > Custom Fields > Contacts. Create four new custom fields: AI Score (number field, 0-100), Lead Tier (text field — will store A, B, C, or D), Score Summary (text field — the AI’s one-sentence qualification summary), and Enriched Industry (text field — from the enrichment step). These fields will be populated by Make.com after scoring and are visible in every contact record.
Create the tier-based automation workflows
In GoHighLevel Automations, create four workflows triggered by custom field value. Workflow 1: trigger when Lead Tier field equals A — action: send internal notification to senior rep with the contact details and score summary, assign the contact to that rep, add to Tier A pipeline stage. Workflow 2: trigger when Lead Tier equals B — action: enrol in standard nurture email sequence, assign to sales queue. Workflow 3: trigger when Lead Tier equals C — action: enrol in long-term drip sequence (monthly touches). Workflow 4: trigger when Lead Tier equals D — action: add tag ‘Unqualified’, add to newsletter list only. These workflows fire automatically once Make.com writes the tier.
The Intelligence Layer
Set up the GHL trigger in Make.com
In Make.com, create a new scenario. Add a GoHighLevel trigger module: Watch Contacts. This module fires every time a new contact is created in your GoHighLevel account. Connect your GHL account using the API key from GHL Settings > Integrations > API Keys. Test the trigger by creating a test contact in GHL — Make.com should detect it within seconds.
Add the Apollo enrichment step
Add an HTTP module after the trigger. Configure it as a POST request to the Apollo API: URL https://api.apollo.io/v1/people/match, header Content-Type: application/json, body: {api_key: YOUR_APOLLO_KEY, email: [GHL contact email from previous step], reveal_personal_emails: false}. Apollo returns a JSON response with the person’s job title, seniority, company name, company size, industry, and LinkedIn URL. Map the relevant fields to named variables for use in subsequent steps. Apollo’s free plan includes 50 enrichments per month — sufficient for testing; their paid plans start at $49/month for higher volumes.
Build the AI scoring step
Add an HTTP module for the Claude API call (or use Make.com’s Anthropic module if available). In the request body pass the model name, max_tokens: 500, and a messages array with your scoring prompt. The prompt: You are a B2B lead qualification specialist. Score this lead 0-100 against our ICP criteria and return a JSON object with: score (integer), tier (A if 75+, B if 50-74, C if 25-49, D if below 25), and summary (one sentence explaining the key qualification factors). Lead data: Name: [name], Company: [company], Job Title: [job title from Apollo], Company Size: [from Apollo], Industry: [from Apollo], Source: [lead source from GHL], Message/Notes: [any form text]. Our ICP: [describe your ideal customer — industry, size, job title, geography]. Return only valid JSON, no other text. Parse the JSON response in Make.com to extract the score, tier, and summary.
Write scores back to GoHighLevel
Add a GoHighLevel Update Contact module. Map: the AI Score field to the score value from the parsed JSON, the Lead Tier field to the tier value, the Score Summary field to the summary, and the Enriched Industry field to the industry from Apollo. When this module runs, the contact record in GHL is updated with all four scoring fields — triggering the tier-based automation workflows you created in Phase 1. Activate the Make.com scenario and test with a real lead submission.
What should my ICP criteria include in the scoring prompt?
Be as specific as possible: the 3 to 5 industries you serve best (not ‘all industries’), the company size range where you deliver the most value (by headcount or revenue), the job titles that typically make or strongly influence the buying decision, the geographies you can serve, and any negative qualifiers (company types or situations that are rarely a good fit regardless of other attributes). The more specific the ICP description, the more accurate the scoring. Review the first 50 scores against actual outcomes and refine the ICP description until the Tier A leads are converting at a notably higher rate.
How do I handle leads with minimal data (no company name, no email)?
Leads with insufficient data for enrichment or scoring receive a default mid-range score and are placed in a manual review queue rather than automatically tiered. Add a router in Make.com: if the email field is empty or the company field is empty, skip the enrichment and scoring steps and instead create a GHL task assigned to a team member to manually qualify the lead. Incomplete data leads are not necessarily bad leads — they simply need a different qualification approach.
Want Your GHL Lead Scoring System Built?
SA Solutions builds complete GoHighLevel + Make.com + AI lead scoring systems — from ICP definition and enrichment setup through scoring workflows and tier-based automation.
