All organizations rely on data as the lifeblood of their business and today, they must contend with an overwhelming volume and complexity of information. Traditional search solutions, while functional, often fail to provide the accurate, context-rich results that modern businesses require in order to make the best decisions for their organization.
Search assistants have come a long way in previous decades, beginning with the most basic keyword-based matching techniques that relied on the manual indexing of information. Rudimental algorithms using rules-based systems meant these tools could better mimic human-like decision-making for information search and retrieval, but the efficacy of these early iterations of AI-powered search assistants was very limited. The early versions of these tools required precisely formatted queries and still heavily relied on keyword matching and sources of carefully indexed, structured data.
The power of AI-powered search
This is where AI-powered search solutions can provide businesses with invaluable assistance. These tools use AI to provide enterprises with an enhanced search experience that gets to the heart of what they are looking for, providing relevant, tailored results using internal data from every corner of the business.
Instead of relying on more traditional keyword-matching techniques, these tools leverage advanced machine learning algorithms, and natural language processing to understand the intention behind a search query, locate the relevant data, and surface actionable insights.
The governing telos of any search tool is to provide accurate, relevant, and useful information for whatever query it is given. Search assistants that take advantage of the full power of AI can provide more relevant and accurate results that users can instantly transform into tangible value. When implemented in an enterprise context, these tools can be trained on internal business data to provide staff with contextually appropriate results, personalized to their organization, allowing them to learn about the business to make better-informed decisions.
With the advent of generative AI, and large language models in particular, these tools can also present the results of a search query in an easily digestible manner. This means employees have access to a conversational, natural-language interface, much akin to a human assistant with total knowledge of all aspects of your business.
Traditional document searches can be time-consuming. While it’s easy enough to do a keyword or keyphrase search in a document or PDF, synthesizing all that information into something meaningful is laborious. There’s also the risk that something is overlooked during the process.
Generative AI search is different. Not only does it lift the process of identifying all the relevant information out of the workload of the human, it can also automatically generate a meaningful, accurate summary from a vast array of sources.
The insight engines take this unstructured data from your enterprise and transform it into actionable knowledge to help drive smarter decision-making in businesses.
The current raft of AI search offerings are not without their drawbacks however, and one inadequacy tools like this suffer from concerns how they store sensitive or confidential business data on cloud-based platforms. Additionally, setting up such solutions can be a complex process requiring a mature data strategy and specialized knowledge to get them configured correctly, and ensure you are getting truly helpful insights on your data.
A turn-key AI solution that can unlock the power of your data
One standout example in the AI search assistant landscape is Cohesity Gaia. Cohesity Gaia is a ready-to-deploy turn-key AI solution that promises to help businesses get enterprise-wide, AI-driven search up and running in minutes to instantly their improve the speed and accuracy of their decision-making.
Cohesity Gaia is more than search.
Gaia uses a retrieval augmented generation (RAG) AI engine, paired with large language models, to allow users to interact with their business data in a natural, conversational manner. Its simple-to-use chat interface eliminates the need for intricate query construction, letting business users quickly fire off questions just like they would in a normal conversation with a human agent. Cohesity Gaia already supports the Azure OpenAI LLM and will be adding other LLMs in the future.
When you add Gen AI to enterprise search, you get not only the ability to search via keywords but also the ability to look in documents and files for semantically aware analysis, the ability to retrieve that information via documents and files, and automatically create summarizations of the results from multiple sources.
Cohesity securely vectorizes your enterprise data, creating a unique, AI-ready index build of your organization’s analytics that is protected with granular rules-based access controls (RBAC). This means users can only access potentially sensitive data, such as personally identifiable information (PII), if they satisfy predefined rules set by the network administrator. Gaia can unify your unstructured data, supporting Microsoft 365, OneDrive, and even NAS data that is protected within Cohesity Data Cloud, with more data sources to be available with future releases.
Gaia is ready to go out of the box. Since the backup data from Cohesity is AI-ready from the offset, the tool helps customers derive new meaning from their backup data and significantly boosts time-to-value by giving them insightful conversational search experiences within minutes or hours. With no further engineering work required, Gaia makes it simple for businesses to safely and securely use AI models on their data without it ever being shared with a third party – including Cohesity.
Through these intuitive question and answer search experiences employees can get insights into a range of topics about their firm, from their organization’s security posture to its customer engagement. Gaia gives users a multi-turn chat interface, meaning users can ask follow-up questions on their organization’s data, helping businesses dive deeper into their datasets, create summarizations and provide more in–depth insights.
For example, Gaia can help security leaders instantly get a top-level assessment of their organization’s level of cyber resilience, surfacing information that would traditionally be buried deep in silos across its IT stack. This saves the user a great deal of time, meaning they don’t need to manually pull together information from different departments and applications, which can be an extremely daunting and time-consuming process in larger enterprises.
Honed by its RAG engine, Gaia’s outputs are highly relevant to the user query, providing citations for the information it has used to build the recommendations it generates context-rich insights. This helps Gaia achieve a high level of explainability, a frequently identified drawback to a number of ‘black box’ AI tools, giving users assurance of the accuracy of its outputs.
The shift towards AI-powered search solutions marks a significant step forward from traditional techniques, and Gaia represents a leap in this journey, making complex data interactions simple and intuitive.
For more information about Cohesity Gaia, visit the solutions overview page here.
