Systems | Development | Analytics | API | Testing

AI and the Future of Finance: Decoding Earnings Calls

In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions is joined by Liam Hynes, Global Head of New Product Development at S&P Global Market Intelligence. They discuss how S&P Global Market Intelligence utilizes AI to analyze corporate earnings calls to guide and improve financial reporting. These insights help businesses enhance communication, refine executive performance, and predict market outcomes. The conversation also highlights the use of Snowflake Cortex AI platform and the importance of data-driven decision-making in the financial sector.

How to Master AI/LLM Traffic Management with Intelligent Gateways

As businesses increasingly harness the power of artificial intelligence (AI) and large language models (LLMs), a new challenge emerges: managing the deluge of AI requests flooding systems. This exponential growth in AI traffic creates what could be considered a gratifying predicament—high demand for your AI services—but also introduces complex challenges that must be addressed for sustainable operations.

How I Built an AI Agent to Automate Customer Support - No Complex Coding Required

What if you could build your own customer service agent that handles real-world support queries automatically, saving time for both customers and our team? Maryam Ahmad from our PRD team did just that using Astera’s low-code and drag-and-drop interface. With Astera AI Agent Builder, Maryam created an intelligent agent designed to handle key customer service tasks for an e-commerce platform: checking order status, cancelling orders, and providing information on refund and return policies, all through a seamless conversational interface.

Building AI Agents: 6 Tips for Success

AI has evolved rapidly—from basic algorithms that suggested content to generative models that create it. Now, we're entering the AI agent era. AI agents refer to sophisticated AI systems that use reasoning and iterative problem solving to achieve specific goals. Instead of waiting for instructions, they adapt and take initiative. Agentic AI has transformative potential for enterprises.

Is Your AI Ready for 2025? AI Automation Testing Strategies and Trends

Artificial Intelligence (AI) is revolutionizing industries worldwide, making it indispensable for modern businesses. However, this rapid growth brings a challenge—traditional testing methods are no longer sufficient to ensure the reliability and quality of complex, data-driven AI systems that are prone to bias. To succeed in 2025, organizations must adopt specialized AI automation testing strategies that validate performance and maintain consumer trust.

Nearly half of testers struggle with AI's learning curve

AI is transforming testing—but not without its challenges. According to the State of Software Quality Report 2025, 46% of testers cite the lack of skilled personnel or the steep learning curve as major barriers to adopting AI in software testing. In this insightful message, we highlight one of the most pressing issues in the QA space: while AI has immense potential to drive efficiency and quality, teams are struggling to fully capitalize on it due to limited expertise.