Algolia today introduced Algolia NeuralSearch™ – a next-generation vector and keyword search as a single API with powerful end-to-end AI handling all search queries. Algolia NeuralSearch understands natural language and delivers extremely precise and relevant results in milliseconds. This technology represents a breakthrough in search and discovery that will revolutionize how people interact with online and app content. Algolia NeuralSearch delivers superior conversions and increased revenue at the scale of demanding enterprise production environments. It leverages advanced Large Language Models (LLM) – the same technology behind ChatGPT and Generative AI – and takes Algolia’s Neural Hashing™ for Hyper-Scale one step further with the ability to continually learn from user interactions, always get better results.
Guillermo Romero, Director Enterprise Architecture, Best Buy Canada , comments, “Algolia’s NeuralSearch technology will help us better understand our customers’ intent to optimize the relevance of our searches across our extensive product catalog. We look forward to integrating this next-generation vector and keyword search technology together with Algolia and providing our customers with a better search and discovery experience.”
Bernadette Nixon, CEO at Algolia , states, “Algolia is dedicated to advancing AI-powered search, and we believe Algolia NeuralSearch can do just that. Algolia NeuralSearch, the first of its kind hybrid search solution, offers users a smarter and more intuitive way to find the most relevant content, anytime, no matter the type of query. Our priority is to facilitate a quick live launch of the platform. We take care of setting up, scaling and managing all search functions and services and do whatever it takes to make search faster and better. In addition, Algolia NeuralSearch is backwards compatible, so customers don’t need to make any technical adjustments to become AI-enabled.”
Frasers Group , which includes a range of fashion brands targeting different audiences, is among the first Algolia customers to use Algolia NeuralSearch in a real-world setting. Kyle Sanders, Head of Digital Optimization, Frasers Group, reports: “We used Algolia NeuralSearch with two of our brands ( Missguided and Isawitfirst) tested and were excited to see a more than 65 percent drop in zero search results and up to a 17 percent increase in conversion rates. These results exceeded our expectations, even though we only forwarded a portion of the searches to Algolia NeuralSearch over a four-week period. Our existing search implementation has been seamlessly optimized to further improve our customers’ discovery journey and make their experience on our website even more engaging – all without having to change a single line of code. We are excited to see what the future holds for Algolia NeuralSearch.”
Algolia NeuralSearch analyzes the relationships between words and concepts and generates vector representations that capture their meaning in an abstract and contextual manner. Because vector-based understanding and retrieval is combined with Algolia’s award-winning full-text search engine, exact attribution is also supported. Algolia NeuralSearch uniquely solves an industry-wide problem in vector search: the inherent limitations of scalability and the high cost of using dedicated computers. To solve this problem, Algolia introduced neural hashing done pioneering work. This technology compresses the search vectors of 2,000 decimal numbers to expressions of static length, which means that the calculation can be carried out very quickly and significantly more economically. Before Algolia’s breakthrough, vector-based search was too computationally intensive to use in production.
Hayley Sutherland, Research Manager, IDC , states, “By adding neural hashing for vectors to the existing keyword search within a single index using an API, Algolia has the potential to perform AI-powered search with significantly improved precision and Revolutionize recall in a way that requires less manual work to set up and update, and lower storage and processing costs.”
Algolia is the only company that uses AI for three primary functions: understanding search queries, query retrieval, and ranking results.
- Understanding Search Queries: Algolia’s advanced natural language understanding (NLU) and AI-powered vector search enable free-form natural language expressions to be interpreted and queries categorized using AI, which prepares and structures a query for analysis. Adaptive learning based on user feedback also refines intent understanding.
- Retrieval: The most relevant results are retrieved and sorted in order from most relevant to least relevant. On retrieval, neural hashing results are merged in parallel with keywords using the same index for easier ranking. This approach solves the “zero results” problem and significantly improves click positions and click-through rates. No other search platform in the search and discovery solutions space offers this powerful capability.
- Ranking: Finally, the best results are moved up. This is done using Algolia’s AI-powered re-ranking, which takes into account the numerous signals associated with the search query (including the exact keyword match score, the contextual personalization profile, the observed article popularity, the score for the semantic agreement, etc.) and learns to achieve maximum relevance.
Few companies have the resources to optimize their search for more than just a few of the most common searches. However, this means that a significant part of the sales potential remains unused. “Across the industry, retailers are missing out on a significant amount of potential orders because of the difficulty in generating revenue from long-tail searches (such as “stunning fall mother of the bride outfit”), which account for up to 55% of all today’s searches,” adds Nixon. “These low-volume searches could add up to millions of searches representing billions of dollars in unfulfilled orders for less popular or searched-for products. Algolia NeuralSearch optimizes all searches, whether frequent or infrequent,
Hayley Sutherland adds: “This evolution from search to discovery through methods like vector search is relevant to the e-commerce and retail industries insofar as it affects product discovery use cases. In the retail world, long-tail searches, meaning less frequently used search terms that may not produce exact keyword matches and return customers zero results, represent lost sales, forcing potential customers to abandon their search and place their order elsewhere . Vector search has become very popular in recent years as it suggests similar or related products to customers when an exact match is not found. It enables customers to find relevant results through natural language queries and helps
When the index changes, new products are added, new content is released, or terms take on new meaning, the AI-powered Algolia NeuralSearch learns and adapts automatically. It requires no additional staff or manual intervention. Depending on the query or search term, keywords or terms – or a combination of both – are automatically assigned. This really puts the search on autopilot.
Rachel Maxwell, Digital Merchandising Manager, Everlane states, “The results from Algolia NeuralSearch were very impressive overall, showing a 9% increase in click-through rates and conversion rate each. We also found that our merchandisers spend less time on manual tasks like creating synonyms to optimize search results and more time on more strategic tasks.”