Advancing Text Search with Neuralex™ Hybrid Embeddings

Executive Summary

Neuralex™ hybrid embeddings represent a breakthrough innovation, seamlessly integrating semantic understanding with precise keyword retrieval in a single vector representation. This enables superior accuracy in identifying named entities within text data, eliminating the need for complex hybrid search architectures and associated computational overhead.

Read our comprehensive test analysis

1. The Challenge in Modern Text Retrieval

In the rapidly evolving landscape of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, semantic vector embeddings have emerged as a cornerstone for text representation. However, these embeddings often fall short in effectively capturing named entities—critical elements such as individuals, organizations, locations, contact details, and domain-specific identifiers—that are essential for applications in information retrieval, question answering, and recommendation engines.

Semantic embeddings excel at conveying overall contextual meaning but frequently fail to prioritize specific entities, resulting in suboptimal retrieval performance.

Consider the following illustrative text:

"Andrej Karpathy hosts a deep learning workshop at Stanford AI Lab on February 20th, 2026 at 10:00am. Contact (650) 555-2341 or +1-408-876-5432. Early bird tickets are $450. Visit ai.stanford.edu/workshops for details."

A conventional semantic embedding accurately reflects the general theme of an AI workshop event but struggles to reliably surface queries for "Andrej Karpathy," "ai.stanford.edu/workshops," or "650-555-2341."

Conversely, traditional keyword-based approaches (e.g., inverted indexes, TF-IDF, BM25) perform well on exact entity matches yet lack semantic awareness, often yielding irrelevant results for queries requiring contextual understanding.

To date, prevailing solutions have relied on hybrid systems combining semantic and keyword search (e.g., via Reciprocal Rank Fusion). While effective in some cases, these approaches introduce significant drawbacks:

  • High costs in development and maintenance
  • Limited scalability
  • Increased query latency
  • Dependence on sophisticated infrastructure and specialized expertise

2. Our Innovative Solution: Neuralex™ Hybrid Embeddings

Neuralex™ addresses these limitations head-on by augmenting leading semantic embeddings—from models by Google, OpenAI, and others—with specialized features derived from HyperDimensional Computing (HDC) techniques. The result is a unified vector that encapsulates both rich semantic context and precise entity detectability.

Neuralex™ introduces a novel paradigm in text search, delivering unparalleled simplicity, efficiency, and performance compared to existing market solutions.

Key advantages of Neurasearch, powered by Neuralex™:

  • Full compatibility with established vector search ecosystems (e.g., PostgreSQL pgvector), facilitating seamless integration into existing infrastructures.
  • Enhanced precision in named entity retrieval, driving superior search relevance and user experience.
  • Significantly reduced computational demands, enabling faster response times and lower operational expenses.
  • Robust scalability for large-scale datasets and high-volume queries.
  • Domain-specific configurability, optimizing performance across verticals such as retail, healthcare, and legal.

Strategic Use Cases

  • E-Commerce: Precise matching on brands, product codes, and attributes (e.g., dimensions, specifications) without reliance on named entity recognition (NER) or part-of-speech tagging.
  • Healthcare: Reliable extraction of patient identifiers, medication names, and clinical details from records.
  • Legal: Efficient identification of case references, statutes, and specialized terminology in vast document corpora.
  • Content Management: Streamlined retrieval of articles by author, publication date, contacts, and other metadata.

These applications demonstrate the transformative potential of Neuralex™ across high-value industries.

3. Rapid Onboarding with Neuralex™

Neuralex operates as a SaaS platform, providing intuitive API access to generate and deploy hybrid embeddings. To begin:

  • Register an account at https://app.neuralex.ca.
  • Retrieve your API key from the dashboard.
  • Experiment with embedding creation and search in the API playground.
  • Incorporate pre-built code samples in your chosen programming language for swift integration.

4. Resources