AWS Kendra

Описание: Reimagine the way you search for information with Amazon Kendra
With Intelligent Search, you can raise productivity, reduce costs, and avoid liability. Amazon Kendra, powered by machine learning, can save your organization millions of dollars in unnecessary costs searching for the right information, while increasing team productivity and informing better decisions. Proven benefits over building search from scratch include:

82% lower 5-year Total Cost of Ownership (TCO) versus building a traditional Enterprise Search Tool
25% increase in employee productivity
80% reduction in development costs

Use cases
1. Enhance internal search experiences for employees
Improve employee productivity and unlock the insights employees need to make data-driven business decisions through a single search interface

2. Improve customer interactions
Reduce contact center costs with intuitive self-service bots, agent-assist solutions, and frictionless document access.

3. Integrate search into SaaS applications
Helps you find information faster with ML-powered in-app searches.

Gilead Accelerates Development of Enterprise Search Tool Using Machine Learning on AWS

Biotechnology company Gilead Sciences Inc. (Gilead) wanted to increase staff productivity and streamline internal data management processes within its pharmaceutical development and manufacturing (PDM) business unit so that it could quickly roll out more therapeutic treatments for people with life-threatening diseases. To work toward this goal, the company wanted to build a scalable enterprise search tool that uses artificial intelligence (AI) and machine learning (ML) to provide predictive analytics and find important documents, knowledge, and data in one centralized location. For the tool to consistently produce relevant results with each natural language query, the company needed a set of solutions that would organize both structured and unstructured data from up to nine enterprise systems and documents from knowledge repositories.

To accelerate its project timeline, Gilead’s PDM team chose Amazon Web Services (AWS), adopting Amazon Kendra, a highly accurate intelligent search service powered by ML. While receiving support from AWS, the PDM team built a data lake within 9 months, and afterward, it built a search tool within only 3 months, completing its project well within its estimated timeline of 3 years. Since launching its enterprise search tool, users across PDM have been able to substantially reduce manual data management tasks and the amount of time it takes to search for information by approximately 50 percent, fueling research, experimentation, and pharmaceutical breakthroughs.