Hubspot Leadgenius

LeadGenius → HubSpot Import API Pipeline

How to fully automate the extraction of enriched leads from LeadGenius and synchronize them natively into a HubSpot portal, with dynamic property group creation and exhaustive AI insight payload parsing.

Table of Contents

0. Overview
1. Prerequisites
2. Creating the HubSpot Private App
3. LeadGenius API Configuration & Bypass
4. LeadGenius JSON Field Reference
5. Creating Custom Properties inside HubSpot
6. Importing Contacts & Data Mapping
7. Creating Saved Views (For SDRs)
8. Complete Automated Execution
9. Troubleshooting

0. Overview

What This Does

LeadGenius Automation API  →  HubSpot Contacts Database
 Over 120 nested objects       Native Custom CRM Properties
  Full X-Admin-Key Bypass      Property Group (LeadGenius AI)

Architecture

The LeadGenius API structure is highly complex, often burying core intel like Company Descriptions and direct phone arrays inside concatenated pipe-separated :Mobile: | :Direct: strings or JSON-wrapped string layers ({"subject": "...", "content": "..."}).

This guide bridges the gap structurally by:

1. Dynamic Authentication: Using X-Admin-Key arrays to completely override LeadGenius standard API throttling bottlenecks.
2. Schema Standardization: Emitting native HTTP POST requests to create a localized leadgenius_data Property Group on HubSpot immediately upon bootstrap.
3. Deep Extraction: Natively unpacking AI analysis, recommendation texts, and formatting integer matrices without UI friction.
4. Massive Deduplication: Launching 100-batch recursive endpoints routing via HubSpot's High-Level Upsert endpoints triggering on standard email objects.

API Strategy Summary

1. Prerequisites

Software Requirements

pip3 install requests python-dotenv

Environment Configuration

Create a local .env file at the root of your execution directory securely:

# LeadGenius
LGP_API_KEY=your_standard_lg_token
LGP_MASTER_KEY=your_admin_bypass_token

# HubSpot
HUBSPOT_API_KEY=your_private_app_bearer_token

2. Creating the HubSpot Private App

In order to seamlessly authorize interactions with your Hubspot infrastructure, you must secure a native Private App token.

1. Click the ⚙️ Settings icon in your HubSpot main navigation bar.
2. In the left sidebar, route to Integrations > Private Apps.
3. Click Create private app.
4. Basic Info: Name your deployment LeadGenius Sync.
5. Under the Scopes tab, explicitly inject these functional read/write headers:
   • crm.objects.contacts.read
   • crm.objects.contacts.write
   • crm.schemas.contacts.read
   • crm.schemas.contacts.write
6. Click Create app and securely port your new Access Token directly to HUBSPOT_API_KEY in your .env.

3. LeadGenius API Configuration & Bypass

Standard integrations fail aggressively on large environments because LeadGenius throttles basic API tokens recursively. We invoke native administrative clearance bypassing this framework utilizing X-Admin-Key.

When querying the core lead engine, we establish dual headers natively:

LG_HEADERS = {
    'X-API-Key': os.getenv('LGP_API_KEY'),
    'X-Admin-Key': os.getenv('LGP_MASTER_KEY')
}
import requests
r = requests.get("https://api.leadgenius.app/api/automation/leads", headers=LG_HEADERS)

[!CAUTION] When X-Admin-Key is utilized natively, the LeadGenius API structurally shifts the standard JSON payload directly inside an isolated data dictionary layer. Any scripts targeting .get('id') will fatally fail unless structurally targeting .get('data', {}).get('id').

4. LeadGenius JSON Field Reference

LeadGenius exports deep multi-layered JSON metadata. We meticulously map these dynamically into HubSpot.

Standard Field Mapping (Base Infrastructure)

Complex AI Extraction Fields (Custom Framework)

Enrichment Target References

5. Creating Custom Properties inside HubSpot

All integrated properties must systematically map to lg_ prefixed tables under a master leadgenius_data component.

Example Schema Execution Array

The following snippet natively configures standard extraction components automatically:

import requests, os
from dotenv import load_dotenv

load_dotenv()
HS_HEADERS = {
    'Authorization': f"Bearer {os.getenv('HUBSPOT_API_KEY')}",
    'Content-Type': 'application/json'
}

def setup_properties():
    # 1. Establish Property Group
    group_req = requests.post(
        'https://api.hubapi.com/crm/v3/properties/contacts/groups',
        headers=HS_HEADERS,
        json={"name": "leadgenius_data", "displayOrder": -1, "label": "LeadGenius AI Data"}
    )
    
    # 2. Extract and Post Objects
    properties = [
        {"name": "lg_ai_score", "label": "LG AI Score", "type": "number", "fieldType": "number", "groupName": "leadgenius_data"},
        {"name": "lg_company_description", "label": "LG Company Description", "type": "string", "fieldType": "textarea", "groupName": "leadgenius_data"},
        # Added extra...
    ]
    
    for prop in properties:
        requests.post('https://api.hubapi.com/crm/v3/properties/contacts', headers=HS_HEADERS, json=prop)

6. Importing Contacts & Data Mapping

Once schema arrays are structurally localized on HubSpot, you bypass the standard REST API limits using High-Volume Endpoints (batch/upsert).

def build_hs_contact(lead):
    # Parses unique JSON strings
    def parse_email_json(text):
        if not text: return ""
        try:
            import json
            parsed = json.loads(text)
            return f"Subject: {parsed.get('subject', '')}\n\n{parsed.get('content', '')}".replace('<br>', '\n')
        except: return str(text).replace('<br>', '\n')

    props = {
        "email": lead.get('email'),
        "firstname": lead.get('firstName'),
        "lg_ai_score": str(int(float(lead.get('aiLeadScore', {}).get('ai_score', 0)))),
        "lg_company_description": lead.get('capture_all_parsed', {}).get('Company Description')
    }
    return {k: v for k, v in props.items() if v}

# Batch Upsert Loop
chunk = leads[0:100]
inputs = [{"idProperty": "email", "id": c['email'], "properties": c} for c in chunk if c.get('email')]

response = requests.post(
    'https://api.hubapi.com/crm/v3/objects/contacts/batch/upsert', 
    headers=HS_HEADERS, 
    json={"inputs": inputs}
)

7. Creating Saved Views (For SDRs)

UI Saved Layouts cannot be programmatically initiated via Standard Endpoints securely. Administrators must instruct internal teams utilizing the following manual pipeline layout configurations:

1. Launch HubSpot and access the Contacts Data Table.
2. Navigate directly to Add View > Create New View and define SDR LeadGenius Outbound.
3. Select Edit Columns and firmly integrate:
   • Lead Status
   • LG AI Score
   • LG Recommendation
   • Mobile Phone
4. Make sure your SDR operators utilize Settings > Objects > Contacts > Record Customization natively to permanently anchor the leadgenius_data sidebar at the apex of user visual fields on the Contact Card.

8. Complete Automated Execution

The unified deployment matrix script integrating Schema Build, Extraction Arrays, Bypass Authentication, JSON Sanitization string manipulation, and Batch Array POSTing natively is available securely within the executed script pipeline.

Simply run recursively:

python3 scripts/lg_full_to_hs.py

9. Troubleshooting