What if your document processing system could do more than categorize documents and extract data, no matter the format? That’s exactly what you can do with intelligent document processing (IDP) software. IDP tools adapt to varying structures and formats and understand content to summarize lengthy documents, identify anomalies, and flag errors. The best part? IDP software continuously improves in accuracy the more you use it.

The financial document processing domain has undergone a 360-degree shift in the past decade. It was at the brink of the 1980s when software providers began releasing document management systems aimed at helping companies save time, money, and effort in regard to financial document processing. What started as simple document management systems using Optical Character Recognition to digitize printed financial documents has evolved into advanced, AI-powered solutions.

Security log management provides an organization with a unified and centralized way of collecting, monitoring, and analyzing different security-related activities and events, fostering easier audits and reporting. It offers organizations, the ability to quickly access and analyze logs to understand the scope and impact of security incidents, facilitate regulatory compliance, and enhance visibility and accountability.

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.

For the ninth consecutive time, Gartner has named Google Cloud Apigee a Leader in the 2024 Gartner Magic Quadrant for API Management.

From social media and Google reviews to sensors and artificial intelligence (AI) assistants, development teams today have access to so much user data, often called big data, that it sometimes feels like a blessing and a curse.

All developers will eventually need to integrate an app with a third-party REST API when doing web development. However, the RESTful APIs that we are trying to make sense of often do not provide a lot of flexibility. This article addresses this issue within your codebase, showing you how to seamlessly implement any Laravel API integration.

From artificial intelligence (AI) to machine learning (ML) to conversational chatbots, the tools we use to interact with and consume information are rapidly changing thanks to powerful new technologies that make understanding our data more accessible than ever. One particularly influential field is natural language technology (NLT) and its branches.

Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.