E-commerce product pages contain a mix of structured specifications, unstructured reviews, and contextual elements like personalized offers or regional variants. Although informative, this volume can lead to cognitive overload, making it difficult for users to quickly and accurately find the information they need. Existing Product Question Answering (PQA) systems often fail to utilize rich user context and diverse product information effectively. We propose a scalable, end-to-end framework for e-commerce PQA using Retrieval Augmented Generation (RAG) that deeply integrates contextual understanding. Our system leverages conversational history, user profiles, and product attributes to deliver relevant and personalized answers. It adeptly handles objective, subjective, and multi-intent queries across heterogeneous sources, while also identifying information gaps in the catalog to support ongoing content improvement. We also introduce novel metrics to measure the framework's performance which are broadly applicable for RAG system evaluations.

It's time to unleash the value of unstructured data The flow of relevant real world data (RWD) to the life sciences industry has exploded in recent years, and most (80%) of that new content comes in the form of unstructured data. Unstructured data includes all the information that is shared in textual narrative and conversational formats. And in the age of Twitter and chatbots, that covers quite a bit of ground. This information can be drawn from social media posts, journal articles, virtual customer contact requests, telehealth conversations, medical notes, and research information – and that is just the short list. This content can be rich in value, but that value is often underutilized because these documents are difficult to manually review, translate, and analyze.

Anthony is recognized as a thought leader and primary innovator of products, solutions, and technologies for the intelligent capture, RPA, BPM, BI and mobile markets. ABBYY is an innovator and leader in artificial intelligence (Al) technology including machine learning and natural language processing that helps organizations better understand and drive context and outcomes from their data. The company sets a goal to grow and strengthen its leadership positions by satisfying the ever-increasing demand for AI-enabled products and solutions. ABBYY has been developing semantic and AI technologies for many years. Thousands of organizations from over 200 countries and regions have chosen ABBYY solutions that transform documents into business value by capturing information in any format.

Robotic process automation (RPA) is gaining traction among enterprises, as RPA tools have proven they can streamline repetitive processes and save lots of time. But as more companies implement RPA, they're also finding they maximize ROI when they pair it with Intelligent Process Automation (IPA). RPA software revenue grew 63.1% in 2018 to $846 million, according to Gartner, making it the fastest-growing segment of the global enterprise software market. RPA tools are used in all industries, although Gartner says the biggest adopters are banks, insurance companies, telcos and utility companies. Such firms typically have many legacy systems and use RPA to help integrate data among them.

In our dealings with customers, one of the most challenging aspects of launching intelligent process automation (IPA) projects is a seemingly simple one: where to start. Whether it's a company that has hit the limits of what robotic process automation can do or is starting from scratch with IPA, in this post we offer three actionable steps to get your project underway. Intelligent process automation, in a nutshell, helps companies automate workflows and processes that involve unstructured content, including text and images found in documents of various formats (PDF, Word, etc.). It enables companies to automate more workflows than they can address with RPA alone, which can only deal with structured content -- like information found in a database. IPA achieves this by applying AI technologies such as machine learning and natural language processing, bringing powerful capabilities to bear.

The concept of dark data sounds ominous, even sinister. But it is very important in the technology world. "To make it more relatable, dark data is like all of the photos on your devices," said Sky Cassidy, who is the CEO of MountainTop Data. "Most of them will never be used or even viewed again, but they are there. So as for dark data, it's all the information companies collect in their regular business processes, don't use, have no plans to use, but will never throw out.

Through integration with Cognitive Services APIs, Azure Search has long had the ability to extract text and structure from images and unstructured content. Until recently, this capability was used exclusively in full text search scenarios, exemplified in demos like the JFK files which analyzes diverse content in JPEGs and makes it available for online search. The journey from visual unstructured content, to searchable structured content is enabled by a feature called cognitive search. This capability in Azure Search is now extended with the addition of a knowledge store that saves enrichments for further exploration and analysis beyond search itself. The knowledge store feature of Azure Search, available in preview, refers to a persistence layer in cognitive search that describes a physical expression of documents created through AI enrichments.

If you're considering Box or just want more information, we're happy to answer all your questions. Simply fill out the form to the right and someone from Box will reach out soon. For immediate assistance, try one of the options below: Headquarters Box, Inc. 900 Jefferson Ave Redwood City, CA 94063 USA 1.877.729.4269

Savvy organizations use the game-changing combination of content and process coupled with cloud AI services to optimize operations and inform strategic decisions. Read on to discover how you can deliver breakthrough user experiences by intelligently mining--and acting on--the data in your content. You can download the entire infographic here or visit the IT Strategist's Guide to Transforming ECM for more advice on effectively mining your content for data. Unstructured content refers to what's produced at your organization every day: presentations, contracts, project plans, spreadsheets, drawings, emails, videos, and more. It's the counterpoint to structured content, which consists of data found in ERP, CRM, line-of-business applications, and other core systems.

Social Market Analytics, Inc. (SMA) aggregates the intentions of professional investors as expressed on Twitter & StockTwits and publishes a series of metrics that describes the current conversation relative to historical benchmarks. Our data is a leading indicator of price movement both positive and negative. There is unique predictive information in unstructured content. Social Market Analytics use AI and Machine Learning techniques developed over the last eight years to convert this unstructured content into data suitable for quantitative analysis. This opens a whole new area of big data analysis.