SEARCH AND ANALYTICS INSIGHTS

At Intra.NET Reloaded Boston, our Managing Director, Kamran Khan, shared insights on how we are combining search with pragmatic AI technologies, specifically Natural Language Processing (NLP) and Machine Learning (ML), to create impactful outcomes in the digital workplace.

For any search application, cloud or on-premises, good insight discovery always starts with good data preparation. We’ll discuss how Accenture Aspire Content Processing can work with Azure Blob Storage and Azure Cognitive Services to enrich unstructured data preparation for a better search and analytics experience. 

The Adobe Experience Manager (AEM) connector is the newest addition to our growing range of connectors designed to support secure data connectivity between search engines and third-party repositories. Read more about the AEM connector's features and use cases.

Enterprises are increasingly seeking innovative systems to better leverage unstructured, natural language content to deliver actionable business insights. We are pleased to introduce Saga – a scalable, easy-to-use natural language understanding framework that enables non-data scientists to create and maintain powerful and flexible enterprise language models for user interaction and document understanding.

Organizations are increasingly becoming insight-driven. Together with the rapid development of AI technologies like machine learning and NLP, the demand for AI-enabled search and analytics solutions will become more prevalent. As we begin 2019, our Managing Director, Kamran Khan, shares some of the most notable observations around these capabilities.

Watch Liam Cavanagh, Azure Search Principal Program Manager, discuss how combining Accenture’s Aspire Content Processing with Azure Cognitive Services helps accelerate unstructured content acquisition, processing, and enrichment for an enhanced Azure Search experience.

In his years as a search engine programmer and architect, our Innovation Lead, Paul Nelson, has come across lots of business-critical as well as creative uses for search engines. Here’s a roundup of 10 prevalent search enterprise use cases he has helped organizations explore and implement over the years.

By implementing enterprise data lakes, companies have started to see multiple benefits. But to gain valuable insights from their data lakes, effective solutions are needed to help users find the right answers and relevant datasets from the massive amounts of data. Watch our Innovation Lead discuss how Natural Language Processing and search can help companies unlock the full value of the data lake.

As the Google Search Appliance (GSA) is approaching its end-of-life, Google has announced its cloud replacement: Google Cloud Search. A Google partner with significant enterprise search expertise, we have been working with Google on the roll-out to learn about Cloud Search features, perform deployments for clients, and provide feedback for future evolution. Read about our engineer's first-hand review of Google Cloud Search.

In this blog post, we discuss how Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques can be used to create knowledge graphs, which can then power chatbots, question/answer systems, and search engines to deliver holistic enterprise knowledge and improve business outcomes.