Systems | Development | Analytics | API | Testing

AI And Real-World Data: A New Era For Identifying And Curing Rare Diseases

Dec 2, 2025 By Snowflake Inc. In Snowflake
In this episode of the "Data Cloud Podcast," Dana Gardner is joined by Chandi Kodthiwada, Vice President of Product Management at Komodo Health, to explore how Komodo Health utilizes vast and disparate data sources to generate unprecedented insights in life sciences and healthcare. They discuss the founding mission of Komodo Health, the challenges of building a comprehensive, de-identified data set, and AI’s role in reducing the burden of disease and improving patient outcomes.
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tricentis

AI-powered test optimization with Tricentis Testim and SeaLights

Dec 2, 2025 By Scott Erlanger In Tricentis
If you find that your team is struggling to get releases out the door, it could be inefficient testing practices. Oftentimes, software teams don’t know what their tests actually cover, or which tests are relevant after each code change — so they run everything. This means spending hours executing full test suites for minor updates or burning through CI/CD resources while bugs slip through untested paths. On top of this, software is always becoming more complex.
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tricentis

How multimodal AI is reshaping software testing

Dec 2, 2025 By Sarah Welsh In Tricentis
Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?
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moesif

How to Build an Internal Chargeback Model for Your API and AI Usage Using Moesif

Dec 2, 2025 By Abu Sakib In Moesif
API and AI services now sit at the heart of modern products. However, the more we use them, the harder it seems to become to account for the budget. Launching an AI product often leads to massive end-of-period bills. This requires attributing costs to the key internal power users and consumption drivers. The challenge is identifying the departments, products, or projects responsible for the consumption, and the extent to which they contribute.
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rainforest

Now Available: AI Test Planner - Rainforest Crawls Your App to Deliver a Ready-to-Use Test Plan

Dec 2, 2025 By James Palmer In Rainforest
Before you can test software, you need to know what to test. That’s where many QA teams stall out. They don’t have the right software testing tools for mapping the app, identifying user paths, and determining testing priorities. So, building a test plan can take days (or more) of manual work. It’s often slow, frustrating, and error-prone.
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iguazio

Introducing MLRun v1.10: New tools for building agents and monitoring gen AI

Dec 2, 2025 By Gilad Shaham In Iguazio
MLRun 1.10, the latest version of our open source AI orchestration framework, is available today to all users. Iguazio started out as a platform to operationalize enterprise machine learning projects. Though we’ve been through quite a few waves of AI in just a short time, the underlying challenges are the same: getting from experimentation to production remains a major blocker.
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antino

RAG Chatbot: The future of Enterprise Knowledge Automation

Dec 2, 2025 By Radhakanth Kodukula In Antino
We’re entering a phase where AI can draft emails, resolve tickets, summarise complex information, and occasionally present fiction as fact with equal conviction. Generative AI has become incredibly powerful, but in enterprise environments, power without precision quickly becomes a risk rather than an advantage. This is exactly where the shift is happening.
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keploy

Generative Ai Testing Tools: The Next Evolution Of Test Automation

Dec 1, 2025 By Himanshu Mandhyan In Keploy
In the last ten years, software testing has advanced significantly, but today’s applications require more than just using conventional forms of automated software testing or entry-level tools that employ artificial intelligence (AI). The rise of microservice architectures, API calls, and continuous deployment has led to another category of software testing products called "Generative" AI Testing tools.
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