Today, we are thrilled to unveil PersanaVector™, our proprietary AI-powered embedding search, now available in beta. This marks the first release in the most significant AI update in our company’s history, revolutionizing how businesses discover, analyze, and engage with the organizations that matter most. The Problem with Traditional B2B Search
Keyword-based search engines were never designed for modern B2B discovery. They rely on exact matches and outdated filters, often failing to understand intent, context, or business relationships.

Why Traditional Search Falls Short:

• Ambiguous queries: Searching for "B2B AI sales tools" may return irrelevant or incomplete results.

• Manual filters required: Manually applying static filters can be tedious, limiting flexibility.

• Keywords miss industry nuances: for example "ML" or "Machine learning" will not capture "reinforcement learning," losing valuable insights.

• Lack of context: Does "data automation" refer to ETL pipelines or CRM workflows?

• Poor relevance: Company descriptions are too broad to enable meaningful ranking.

How PersanaVector™ Solves This

With AI-powered embeddings, PersanaVector™ replaces static keyword matching with semantic intent recognition, delivering highly relevant companies, competitors, and partners based on meaning—not just keywords.

Key Benefits of AI-Powered Search:

• Understands intent: Whether searching for "AI sales engagement" or "data enrichment for B2B," PersanaVector™ finds the right companies effortlessly.

• Industry-aware embeddings: Trained on millions of company profiles, ensuring high relevance.

• Instantaneous results: Lightning-fast similarity search across 100M+ embeddings.

The Technology Behind PersanaVector™: Proprietary AI Embeddings for B2B Search

Unlike generic vector models, PersanaVector™ is uniquely trained on real-world B2B data. It learns how companies relate to each other, ensuring results that make sense for sales, marketing, and competitive intelligence teams.

Real-Time Search with Memory-Efficient Indexing

Built on state-of-the-art Approximate Nearest Neighbors (ANN) technology, PersanaVector™ ensures:

• Millisecond-fast search, even across 100M+ embeddings.

• Dynamic indexing that continuously adapts to market changes such as funding, revenue, and more.

• Optimized HNSW settings for high recall and low compute costs.

Intelligent ICP & Lookalike Matching

PersanaVector™ goes beyond simple search—it infers relationships and identifies lookalike prospects, enabling powerful business insights:

• AI-powered Ideal Customer Profiles (ICP): Instantly find companies similar to your top customers.

• Market cluster discovery: Identify emerging competitors before they break out.

• Automated industry tagging: AI dynamically maps company relationships instead of relying on outdated industry taxonomies.

The Hybrid AI-Powered Search Approach

The core of PersanaVector™ is its hybrid search architecture, which combines:

• Semantic vector search: Understanding the meaning and intent behind queries.

• Traditional attribute-based filtering: Leveraging structured business data.

• Custom relevance ranking: Prioritizing results based on business significance.

This approach delivers 76% higher relevance scores compared to standalone keyword search or vector embeddings alone.

Our Embedding Training Approach

What makes PersanaVector™ uniquely powerful is our systematic approach to embedding training:

• Specialized B2B training data: We meticulously curated datasets combining:

  
  

  • Company descriptions across various stages and industries.

  • Product documentation and technical specifications.

  • Funding announcements and investor presentations.

  • Industry reports and market analyses.

  • Job descriptions and role requirements.

    
    

• Domain-specific fine-tuning: Unlike general-purpose embeddings, we've optimized specifically for:

  
  

  • B2B terminology recognition.

  • Industry-specific jargon comprehension.

  • Business model classification.

  • Product category understanding.

  • Technology stack identification.

    
    

• Continuous refinement: Our embeddings evolve through:

  
  

  • Feedback loops from user interactions.

  • New company data incorporation.

  • Emerging industry terminology adaptation.

The result is an embedding model that truly understands B2B contexts, enabling more accurate company matching, competitor identification, and market mapping than generic approaches.

Optimized Vector Indexing

To achieve millisecond-level performance, PersanaVector™ employs:

• Hierarchical Navigable Small World (HNSW) indexing for sub-20ms query times across 100M+ vectors.

• Adaptive quantization to compress vectors efficiently while maintaining accuracy.

• Distributed index sharding to ensure scalability across computing resources.