AWS launches Amazon Kendra, an ML powered enterprise search service

Amazon Web Services (AWS), an Amazon.com company, 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 of 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 centers can use Amazon Kendra to find the right answer to customer questions across the complete library of support documentation.

Amazon Kendra requires no machine learning expertise and can be set up completely within the AWS Management Console.

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, don’t provide natural language queries, and can’t 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.

“Our customers often tell us that search in their organizations is difficult to implement, slows down productivity, and frequently doesn’t work because their data is scattered across many silos in many formats. Using keywords is also counterintuitive, and the results returned often require scanning through many irrelevant links and documents to find useful information,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning, Amazon Web Services.

“Today, we’re excited to make Amazon Kendra available to our customers and enable them to empower their employees with highly accurate, machine learning-powered enterprise search, which makes it easier for them to find the answers they seek across the full wealth of an organization’s data.”

Amazon Kendra encrypts data in transit and at rest and easily integrates with commonly used data repository types such as file systems, applications, Intranet, and relational databases, so developers can index their company’s content with just a few clicks, and provide end-users with highly accurate search without writing a single line of code.

Amazon Kendra provides a wide range of native cloud and on-premises connectors to popular data sources such as SharePoint, OneDrive, Salesforce, ServiceNow, Amazon Simple Storage Service, and relational databases, with more being added throughout this year.

Developers can quickly and easily add data sources to their Amazon Kendra search index by selecting the connector type, and those connectors will maintain document access rights. Data connectors can be scheduled to automatically sync between the index and data sources to ensure end-users are always securely searching the most up to date content.

Amazon Kendra also helps to ensure that search results adhere to existing document access policies by scanning permissions on documents, so that search results only contain documents for which the user has permission to access.

Developers simply log into the Amazon Kendra console, point the service at their unstructured and semi-structured documents, and Amazon Kendra then creates an index across silos of data.

Customers can then deploy Amazon Kendra across their applications from the console by copying short code samples provided in the documentation. Amazon Kendra is available in US East (N. Virginia), US West (Oregon), and EU West (Ireland), with other regions coming soon.

3M is a multinational corporation and a leading manufacturer of products including abrasives, chemicals and advanced materials, films, filtration, adhesives, and more. 3M applies science in collaborative ways to improve lives, daily.

“Research and development is the heartbeat of 3M, and we invest deeply in the science that makes us strong. When our material scientists lead new research, they need to access past research that may be relevant. This information is often buried in our patents and expansive knowledge repositories,” said David Frazee, Technical Director, 3M Corporate Research Systems Lab.

“Finding the right information is often exhausting, time consuming, and sometimes incomplete. With Amazon Kendra, our scientists find the information they need quickly and accurately using natural language queries. With Kendra, our engineers and researchers are enthusiastic about the ability to quickly find information which will enable them to innovate faster, collaborate more effectively, and accelerate the ongoing stream of unique products for our customers.”

PwC is a network of firms in 157 countries with over 276,000 people who are committed to delivering quality in assurance, advisory, and tax services. “PwC designed RegRanger for regulated industries, providing access to regulatory and compliance information as well as proprietary PwC insights,” said Chris Curran, Partner and Chief Technology Officer of PwC’s New Ventures organization.

“Our goal is to help our customers get to the answers they need faster – even when the right answers may be buried within documents over 100 pages long – so they can understand regulatory information faster and make decisions more quickly and confidently. As an early adopter of Amazon Kendra, PwC is now developing and testing enhanced search capabilities to be implemented in our next version of RegRanger.

“These enhanced capabilities will allow users to ask natural language questions, which is a dramatic improvement over traditional keyword searching methods and manual reviews of documents. We are excited about the added value that Kendra will bring to our customers in regulated industries.”

The Allen Institute is fiercely committed to solving some of the biggest mysteries of bioscience, researching the unknown of human biology, in the brain, the human cell, and the immune system. At the same time, they are pushing the frontiers of bioscience to continue to explore the edges of scientific discovery.

“One of the most impactful things AI like Amazon Kendra can do right now is help scientists, academics, and technologists quickly find the right information in a sea of scientific literature and move important research faster,” said Dr. Oren Etzioni, Chief Executive Officer of the Allen Institute for AI.

“The Semantic Scholar team at Allen Institute for AI, along with our partners, is proud to provide CORD-19 and to support the AI resources the community is building to leverage this resource to tackle this crucial problem.”

Baker Tilly is a leading advisory, tax, and assurance firm dedicated to building long-lasting relationships and helping customers with their most pressing problems — and enabling them to create new opportunities.

Baker Tilly works with clients on rationalizing their data to provide insights on market conditions and customer preference and trends, thus enabling them to quickly anticipate and adapt to change.

“Amazon Kendra provides direct connection with unbelievable levels of efficiency and accuracy. We found that by using Kendra, our clients are able to surface relevant information 10 times faster when compared to SharePoint full text search,” said Ollie East, Director of Advanced Analytics and Data Engineering at Baker Tilly.

“As an example, Amazon Kendra allows product managers to ask questions in everyday language such as ‘What parts are made of titanium?’ quickly surfacing an answer such as a list of relevant product manuals, technical bulletins, service alerts, and patent registrations previously not possible with keyword search and connecting them to relevant content across an enterprise-wide repository, or providing marketing managers quick access to crucial research on customer behavior.”

Onix, an award-winning cloud consulting company with nearly 20 years of deep enterprise search experience, has helped hundreds of customers adapt to the ever-changing search landscape.

“Search capabilities have evolved over the years. Users now expect the same experience they get from the semantic and natural language search engines and conversational interfaces they use in their personal lives,” said Tim Needles, President and CEO at Onix.

“Powered by machine learning and natural language understanding, Kendra improves employee productivity by up to 25%. With more accurate enterprise search, Kendra opens new opportunities for keyword-based on-premises and SaaS search users to migrate to the cloud and avoid contract lock-ins.”

Haufe Group takes companies into the digital age and creates the workplace of the future, with competence, passion, and experience. “At Haufe Group we continuously explore ways to improve the customer experience and seek to increase employee productivity through better technology. One key element to being efficient in today’s working environment is fast access to relevant data, including bringing together data that resides on distributed systems.

“Everybody has experienced challenges in finding the right information in the right place at the right time. Often, we are frustrated by the effort associated with finding the data we are searching for. Poor search experiences compound themselves when the search is performed on text-based content, especially for document search,” said Andreas Plaul, Head of ICT Services at Haufe Group.

“While there are already various approaches in place to optimize this search experience at Haufe Group, we are confident that Amazon Kendra will contribute significant additional optimization, such as providing a single search experience across at least seven key information repositories across the group and will be key to achieving the speed and ease of access we desire.

“With its predefined connectors for different data repositories, and the option to create custom connectors, we expect Amazon Kendra to help with a broad range of use cases for improved customer experience and employee productivity.”