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Elastic shows the power of Search AI platform

Elastic shows the power of Search AI platform

We were recently guests at Elastic in Amsterdam. This software company with Dutch roots has built its Search AI platform for about 12 years. Thanks to the introduction of generative AI, searching structured and unstructured data from various sources is becoming increasingly relevant. Elastic’s Search AI Platform serves three primary use cases: search, security, and observability.

At the ElasticON event, we attended in late November, founder and CTO Shay Banon gave an extended keynote to explain how Elastic has grown into an ideal foundation for these three areas, something he could oversee well from various positions. He has held the highest technical position since early 2022 while previously being CEO. No one could have foreseen that 2022 would be a turning point in technology with the rise of generative AI.

So, Banon indicated at the beginning of his keynote that Elastic had made significant technical changes in the past two years. Indeed, as a widely used open-source technology, Elasticsearch had to find ways to meet new customer needs arising from generative AI and trends in the database industry decoupling computing from storage. Both developments have led to a new focus for Elastic: how do you stay relevant as a platform in an era where data, search functionality and AI are converging?

Search technology is more relevant than ever before

With its focus on search, security and observability, Elastic sees increasing demand from businesses for technology that helps leverage structured and unstructured data. Meanwhile, AI models are becoming more prevalent in everyday business processes. With these principles, Elastic remains true to its mission of making data accessible while further integrating generative AI within the platform.

Banon formulated these technical goals during the keynote address for developers and security experts. Elastic’s technical depth is widely known and a plus, which is why IT professionals love using Elastic software. A powerful Search AI engine that protects proprietary data is a must-have to achieve this.

According to Banon’s keynote, generative AI did not come as a surprise. Before the rise of generative AI, sceptics claimed that traditional search solutions would become obsolete. According to Elastic, advanced search technology, such as semantic search, is an indispensable building block for AI applications.

Presentatieslide met de titel "Search AI is The Way" met drie secties: Elastic integreert AI met zoeken, biedt volledige mogelijkheden en is lid van het open AI-ecosysteem.

Key to smart solutions

The strength of Elastic’s solutions lies in the combination of speed, scalability and precision. Semantic search plays a key role here. This allows search engines to understand and interpret the intent and context behind search queries. Instead of simply matching keywords, semantic search focuses on understanding the meaning behind a query. To do this, semantic search looks at precisely what the user means and the search query’s context.

In particular, vector search is an important technique that supports this semantic approach. Here, searches and documents are converted into numerical representations – so-called embeddings. Algorithms then analyze these embeddings. On this basis, a keyword can be linked to its underlying context and relationship to other concepts. For example, the keyword “banana” can be associated with nutrition, health, vitamins and energy. The result is a search experience that accurately responds to the user’s intentions. The Search AI engine is relevant not only for applications such as e-commerce but also for critical business processes such as customer service, fraud detection and IT security.

An important aspect of this is its application to proprietary data. Elastic enables companies to apply semantic search to their proprietary datasets without data leaving the secure environment. This is essential in industries where data privacy is a priority, such as healthcare and finance. With this approach, companies can benefit from the insights of advanced AI models while remaining compliant with strict data privacy regulations.

The Elastic platform can also build generative AI experiences using LLMs. This allows generative AI models to securely reference proprietary enterprise data in generated responses and conversational queries, according to Banon, making generative AI applications more relevant than ever.

Back to open source roots

One of the most important announcements during ElasticON should not go unmentioned. Elastic announced that Elasticsearch, currently the most widely used vector database worldwide and data visualization tool Kibana are once again fully open-source products since the change in the licensing model in 2021. This is due to adding the Affero General Public License (AGPL) license alongside the existing ELv2 and SSPL licenses. With this move, Elastic wants to show its commitment to transparency and accessibility.

Presentatieslide met het "Search AI Platform" met iconen voor de fasen: Ingest, Process, Storage & Replication, Search, AI & ML Analysis, Visualization, Workflow Automation. Een spreker staat aan de zijkant.

Founder Banon emphasized during ElasticON that open source has always been a fundamental part of Elastic. The new AGPL license ensures that Elasticsearch can again be classified as open source according to the guidelines of the Open Source Initiative (OSI). With this, Elastic offers users flexibility and contributes to the broader discussion about modern open-source licensing. For existing users, nothing changes: they can continue with the license they are already using. New users, however, can choose an OSI-approved open-source license. This shows how Elastic makes data and AI accessible to various applications.

Versatile platform

With innovations and a focus on search, security and observability, Elastic offers a Search AI platform. Elastic also plays a significant role in IT security. The software company has been active in this sector for years, but it all started with a search. Now it also plays a big role in security by offering tooling for SIEM (Security Information and Event Management). These tools promise to go beyond just analyzing log data; they identify and neutralize potential threats before they can do damage.

In addition, Elastic has made significant progress within observability by enabling IT teams to monitor systems in real-time. This is essential as the complexity of IT environments increases exponentially, and downtime has immediate financial consequences. Elastic integrates with OpenTelemetry to make data collection from different systems and platforms easier in this context. Thus, Elastic avoids vendor lock-in and enables companies to integrate data into their observability and security solutions.

The Elastic Search AI platform makes data accessible and actionable by combining logs, metrics, and traces. This allows IT teams to monitor, troubleshoot, and optimize business processes, in addition to the traditional search functionalities it all started with. As far as we are concerned, this gives Elastic a rich past on which it can build and, simultaneously, moves into a promising future.

Also read: Elastic Cloud Serverless enhances real-time search