Discover the Future of Text Search with Neuralex™ Hybrid Embeddings

TL;DR:

Neuralex hybrid embeddings fuse powerful semantic understanding with precise keyword matching — all in a single, efficient vector. Say goodbye to frustrating misses on named entities, and hello to lightning-fast, ultra-accurate retrieval in your text data. No more clunky hybrid systems or sky-high compute costs!

Try it now >

The Problem: Why Traditional Search Falls Short

In the explosive world of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) apps, semantic vector embeddings have been the go-to hero for capturing text meaning. But here's the catch — they often completely whiff on named entities like people, places, brands, phone numbers, or URLs. These are the gold nuggets in so many real-world applications: information retrieval, Q&A, recommendations, you name it!

Semantic embeddings nail the big picture vibe, but when users hunt for specifics, results flop.

Check out this real-world example:

"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 standard semantic embedding captures "AI workshop event" perfectly... but search for "Andrej Karpathy" "ai.stanford.edu," or "650-555-2341"? - Crickets. Those critical details get lost.

Flip to old-school keyword search (inverted indexes, TF-IDF, BM25) — great at spotting exact matches, but zero grasp of context. Query something nuanced, and you drown in irrelevant noise.

Until now, the "fix" has been hybrid setups: blending semantic + keyword search (think Reciprocal Rank Fusion). Sounds smart, right? But in reality, they're a headache:

Expensive to build and maintain
Often don't scale well
Add painful latency to queries
Demand complex infrastructure and rare expertise

Enough already — there's a better way!

Meet Neurasearch: Precision and Simplicity in One Hybrid Vector

Enter Neurasearch — the breakthrough that supercharges state-of-the-art semantic embeddings (from giants like Google and OpenAI) with cutting-edge HyperDimensional Computing magic. We "augment" them with specialized features, packing both deep semantic meaning and razor-sharp entity detection into a single, powerful vector.

No separate keyword indexes. No fusion algorithms. No extra latency — just better results, faster and at lower cost.

This isn't just an improvement — it's a revolutionary leap in text search. Neurasearch delivers simplicity, speed, and performance that crushes most competitors!

With Neurasearch, the standout product from Neuralex, you get:

Seamless integration with your existing vector databases (like PostgreSQL pgvector) — drop it in and go!
Skyrocketed accuracy for named entities, meaning happier users and spot-on results every time.
Slashing compute and integration costs with blazing-fast queries and lower overhead.
Effortless scaling for massive datasets and traffic spikes — no sweat.
Customizable power tailored to your domain (e-commerce, healthcare, legal, and beyond).

Exciting Use Cases:

E-commerce: Supercharge product discovery! Nail searches by brand, model numbers, specs like size or weight—all without messy NER tagging.
Healthcare: Unlock medical records instantly by pinpointing patient names, meds, and treatments with precision.
Legal: Speed through documents, effortlessly surfacing case names, statutes, and key terms.
Content Management: Turbo-boost CMS search — grab articles by author, dates, contacts, and more in a flash.

The possibilities are endless!

Jump In Today with Neuralex Embeddings

Neuralex is your easy-access SaaS platform for generating your embeddings via a slick API. Ready to level up? Here's how to dive in:

Sign up instantly on the Neuralex platform.
Grab your API key from the dashboard.
Play around in the API playground to see the magic firsthand.
Snag ready-to-use code snippets in your favorite language and integrate seamlessly.

It's that simple — transform your search game today!