How does AI lead scoring work in the qualification engine?

The engine scores leads across 5 dimensions: Title Authority (C-suite=5 to Individual=1), Email Quality (verified vs predicted vs unknown), Completeness (percentage of fields populated), LinkedIn presence (profile found or not), and Target Match (industry/role fit). These are weighted and combined into a 0-100 score for AI outbound automation prioritization.

How does persona classification improve AI outbound automation?

Contacts are classified into 4 personas: Economic Buyer (has budget authority), Champion (internal advocate), Technical Evaluator (technical decision maker), and Influencer (can recommend but not approve). This enables multi-threaded outreach strategies targeting the right stakeholders.

Does the engine support international contacts for global AI lead generation?

Yes. Multi-language name normalization handles Latin, CJK (Chinese/Japanese/Korean), Cyrillic, and Arabic scripts. Language detection runs automatically, ensuring correct name parsing regardless of the contact's origin in the lead enrichment platform.

AI Qualification Engine - Lead Scoring & AI Outbound Automation

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

14-rule junk removal for AI lead generation: missing names, invalid emails, role emails (info@, support@), navigation text, UI elements, social handles misidentified as names, generic contacts, noise strings.

Step 3

Score

5-dimension scoring for AI outbound automation: Title Authority (C-suite=5, VP=4, Director=3) + Email Quality (verified=high, predicted=medium) + Completeness (how many fields filled) + LinkedIn enrichment (profile found) + Target Match (industry/role fit).

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

Step 3

Score

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

Step 3

Score

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.

LeadsLogix Dashboard

Live

+12%

24,847

Leads

+8%

18,293

Verified

+15%

6,142

Companies

+22%

$2.8M

Pipeline

Pipeline78%

Discover

Crawl

Extract

Verify

Score

FAQ

Frequently Asked Questions

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Contact Intelligence Engine Email Intelligence Engine Export Center

Start Using the Actor Engine 4 of 6 Engine

Deploy this engine in minutes. No configuration required — just provide your data and let the pipeline handle the rest.

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

Step 3

Score

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

Step 3

Score

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.

Step 1

Normalize

Language detection (Latin/CJK/Cyrillic/Arabic), name normalization, email format validation, phone standardization for the B2B sales intelligence pipeline.

Step 2

Clean

Step 3

Score

Step 4

Output

Cross-company deduplication (email-based + name+company matching), priority classification (High/Medium/Low), persona labels, streaming dataset push to the sales intelligence API.