How Vector Similarity Search is Revolutionizing Data Analysis: Insights from AI Experts
The era of unstructured data has arrived. With vast datasets of images, text, videos, and sensor data, traditional data retrieval methods fall short. This is where vector similarity search comes in. It's a game-changing technology that's transforming how organizations search, analyze, and extract value from large, unstructured datasets.
To shed light on this transformative field, we draw on insights from two industry leaders — Daniel Svonava, co-founder of Superlinked, and Jacky Koh, founder of Relevance AI. These experts explore how vector similarity search is driving breakthroughs in AI, machine learning (ML), and data analysis.
If you’ve ever wondered how Netflix recommends shows, how Google finds images based on text, or how Spotify curates your playlists, then you’ve already seen vector similarity search in action. This article explains how it works, its practical applications, and why it’s rapidly becoming a must-have tool in AI development.
What is Vector Similarity Search?
At its core, vector similarity search is about finding similar items in a dataset. Unlike traditional search (where you type exact text queries), vector search lets you query by meaning, not exact words.
To understand it, imagine each data point (like a text document, image, or sound) is represented as a vector — a list of numbers that describes the content's key characteristics. Similar content has vectors that are "close" to each other in high-dimensional space.
For example:
A search query for "red sneakers" will return images of sneakers that look visually similar to "red sneakers" — even if the image labels don't say "red" or "sneakers."
A search for "positive reviews" in a customer feedback database can locate reviews that have a similar positive sentiment — even if the exact words aren't present.
How Does Vector Similarity Search Work?
Here's a simplified workflow:
Data Embedding: Each data item (image, text, video, or sound) is converted into a vector embedding. These embeddings capture the semantic meaning of the data. AI models like BERT (for text), CLIP (for images), and Sentence Transformers create these embeddings.
Vector Indexing: The embeddings are stored in a vector database like Pinecone, Weaviate, or Milvus. Instead of storing the raw data, the database stores the vectors for each data point.
Similarity Calculation: When you search for a new item (like a search query or image), it’s also converted into a vector. The database finds the closest matching vectors using mathematical measures like cosine similarity, Euclidean distance, or dot product.
Results Return: The system returns the items (like images, videos, or documents) with the most similar vectors to the search query.
This process is extremely fast, even for datasets containing millions or billions of items. Vector databases like Pinecone and Milvus use optimized indexing and search techniques, such as approximate nearest neighbor (ANN) algorithms, to ensure fast query results.
Applications of Vector Similarity Search
Vector similarity search isn't limited to one domain — it’s revolutionizing industries from e-commerce to biotech. Below are some of the most important use cases.
1. Semantic Search & Recommendation Engines
Problem: Traditional search engines rely on exact keyword matches, but users often search for concepts, not exact words.
Solution: Vector search powers semantic search, where AI understands the user's intent and retrieves conceptually similar results.
Example:
E-commerce: Users searching for “affordable black sneakers” are shown similar sneakers, even if the product titles don't contain those exact words.
Streaming Services: Platforms like Netflix and Spotify use embeddings to recommend shows, songs, and movies that are “similar” to what you've watched or listened to.
2. Visual Search & Image Recognition
Problem: How do you find an image in a database when you don’t have a description for it?
Solution: Vector embeddings from models like CLIP encode images into vectors. If you query for "pictures of cats," the system retrieves images with similar visual features.
Example:
Pinterest: Reverse image search allows users to upload an image and find visually similar images.
Fashion Retail: Upload a photo of a jacket, and AI shopping assistants find similar jackets in the store's inventory.
3. Document Clustering & Content Discovery
Problem: How do you cluster millions of documents that don’t have labels?
Solution: By converting documents into vector embeddings (using models like BERT or OpenAI's Embedding API), companies can group and cluster similar content.
Example:
Research Discovery: Universities can search vast research repositories using natural language queries, surfacing research papers based on conceptual meaning.
Content Recommendations: Platforms like YouTube cluster related videos based on user watch behavior.
4. Fraud Detection & Anomaly Detection
Problem: How do you detect abnormal behavior in a data stream?
Solution: By embedding normal behavior into vectors, companies can identify outliers and flag unusual patterns.
Example:
Financial Services: Embedding customer spending behavior can help banks flag fraudulent transactions.
IoT Sensors: IoT devices use embeddings to detect unusual patterns from connected devices, predicting when machines may fail.
5. Personalized Learning & Adaptive AI
Problem: How can AI systems offer personalized learning experiences?
Solution: Systems like Duolingo or Coursera embed user behavior and course content into vectors, personalizing the learning journey.
Example:
Adaptive learning platforms track which lessons a student has mastered. Vector similarity search ensures students see concepts that are most relevant to their learning journey.
How Are Companies Like Superlinked and Relevance AI Leading the Way?
Superlinked and Relevance AI are two trailblazing companies leading innovation in this space.
Superlinked: Co-founded by Daniel Svonava, Superlinked specializes in AI-powered customer experiences. By embedding customer interactions and search behavior, they enable hyper-personalized user journeys on e-commerce platforms, SaaS tools, and community platforms.
Relevance AI: Founded by Jacky Koh, Relevance AI provides vector-based data analytics and visualization tools. Their platform allows businesses to analyze unstructured data (like customer reviews) using vector embeddings, identifying hidden insights that drive business decisions.
Both companies are pioneers of vector similarity search, offering advanced tools to make unstructured data usable, searchable, and actionable.
Tools & Technologies for Vector Similarity Search
If you’re ready to get started with vector search, here are some of the top tools and libraries:
Vector Databases: Pinecone, Weaviate, Milvus, FAISS (Facebook AI Similarity Search)
Embedding Models: BERT, CLIP, OpenAI Embeddings, Sentence Transformers
Search Engines: Elastic Vector Search, Vespa, Google’s ANN (Approximate Nearest Neighbor) Algorithms
These tools allow companies to build semantic search engines, recommendation systems, and AI-driven content discovery tools in days, not months.
What’s Next for Vector Similarity Search?
Multimodal Search: New AI models (like OpenAI's CLIP) allow cross-modal queries. You can search for images using text (like "find images of a happy dog") and get results from an image database.
Larger Datasets: Companies are now building vector databases with billions of entries. As computing power increases, even larger datasets can be searched in real-time.
AI-driven Search Experiences: Voice, video, and AR searches will leverage vector search for a more natural, intuitive experience. Imagine searching for items with your voice or camera instead of typing.
Conclusion
Vector similarity search is one of the most exciting and disruptive technologies in AI today. By converting unstructured data into vectors, companies can perform semantic search, recommendation, and anomaly detection at scale.
With pioneers like Daniel Svonava (Superlinked) and Jacky Koh (Relevance AI) leading the way, vector similarity search is making search smarter, faster, and more intuitive.
Whether you’re an AI enthusiast, a tech professional, or a data scientist, this is a technology to watch. As data volumes explode, vector similarity search will be the engine that powers the next wave of AI-driven search and discovery.