Corporate Wide Search

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. 

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.

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.

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.

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.

Solr vs. Elasticsearch has been discussed so frequently on our blog and within the enterprise search community. But as traditional enterprise search has evolved into what Gartner calls “Insight Engines,” we revisited this topic to provide the latest observations, including cloud deployments, integration with big data analytics, and cognitive search capabilities. Read on to find out the key criteria to consider if you’re weighing between Solr and Elasticsearch in 2018.

Developed by our team at Accenture Applied Intelligence and our partner Microsoft, this demo leverages Aspire Content Processing and Azure Cognitive Services to enhance unstructured data preparation, enabling advanced, cognitive search functionalities in Azure.