Amazon Web Services (AWS) has announced the general availability of Amazon Kendra, a highly accurate and easy to use enterprise search service powered by machine learning. With just a few clicks, Amazon Kendra uses machine learning to enable organizations to index all their internal data sources, make that data searchable, and allow users to get precise answers to natural language queries.
When users ask a question, Amazon Kendra uses finely tuned machine learning algorithms to understand the context and return the most relevant results, whether that be a precise answer or an entire document. For example, businesses can use Amazon Kendra to search internal documents spread across portals and wikis, research organizations can create a searchable archive of experiments and notes, and contact centres can use Amazon Kendra to find the right answer to customer questions across the complete library of support documentation.
Despite many attempts over many years, searching for information within an organization remains a vexing problem for today’s enterprises. Many businesses and organizations struggle implementing internal search across their siloed troves of data, requiring their end-users to use keywords to find information. Organizations have vast amounts of unstructured text data, much of it incredibly useful if it can be discovered, stored in many formats, and spread across different data sources (e.g. SharePoint, Intranet, Amazon Simple Storage Service, and on-premises file storage systems).
Even with common web-based search tools widely available, organizations still find internal search difficult because none of the available tools do a good job indexing across existing data silos, do not provide natural language queries, and cannot deliver accurate results. When end-users have questions, they are required to use keywords that may appear in multiple documents in different contexts, and these searches typically generate long lists of random links that end-users have to sift through to find the information they seek – if they find it at all.
Amazon Kendra reinvents enterprise search by allowing end-users to search across multiple silos of data using real questions (not just keywords) and leverages machine learning models under the hood to understand the content of documents and the relationships between them to deliver the precise answers they seek (instead of a random list of links).
Because natural language understanding is at the core of Amazon Kendra’s search engine, employees can run their searches using natural language (keywords still work, but most users prefer natural language searches). As an example, an employee can ask a specific question like “when does the IT help desk open?” and Amazon Kendra will give them a specific answer like “9:30 AM,” and highlight the passage in the source document where it found the answer, along with links back to the IT ticketing portal and other relevant sites.
Amazon Kendra is also optimized to understand complex language from multiple domains, including IT (e.g. “How do I set up my VPN?”), healthcare and life sciences (e.g. “What is the genetic marker for ALS?”), and insurance (e.g. “How long does it take for policy changes to go into effect?”).
Currently, Amazon Kendra supports industry-specific language from IT, healthcare, and insurance, plus energy, industrial, financial services, legal, media and entertainment, travel and hospitality, human resources, news, telecommunications, mining, food and beverage, and automotive, with additional industry support coming in the second half of this year.
