Keyhole Software partnered with a national grocery distribution organization to accelerate the modernization of its legacy COBOL systems by implementing a cloud-native microservices architecture on Microsoft Azure. In an environment with limited DevOps maturity, Keyhole played a pivotal role in establishing both the technical foundation and cultural framework for scalable, developer-driven software delivery and ongoing digital transformation. Our Approach A …
AI is changing how mobile apps work, and it no longer requires constant internet access. With on-device machine learning, mobile applications can run AI features directly on the device. This approach improves performance, enhances data privacy, and allows apps to function even without a network connection. At Keyhole Software, we help businesses explore the best way to integrate AI into …
So, you want to develop a new website with spiffy apps on Android and iOS, and you want that website and your APIs to run in Node Express. It sounds like a lot of work to write the website in React (or Flutter or whatever the language de jour is), the Android app with Android Studio, and the iOS app with Xcode…
Parts of this blog, including the code examples and architecture frameworks, were generated using Keyhole Software’s EnterpriseGPT web client—an open-source tool that empowers organizations to deploy user-friendly chatbot solutions with ease. Interested in exploring further? Contact our team, and we’d be happy to provide credentials for you to try it out. The Rise of Python and Other Languages We’ve seen …
Debbie Reynolds joins Zach on the podcast today. Debbie has been involved in digital transformation for decades across a variety of industries. She found her niche in data privacy, working on the bleeding edge with corporations as large as McDonald’s, helping them prepare for the General Data Protection Regulations (GDPR). Eventually, she started her own consulting firm, focused on counseling companies around data privacy compliance and strategy.
Debbie’s views on Generative AI are, of course, filtered through the lens of privacy. She cautions her clients, big and small alike, to be wary of the confidentiality of the information they input into LLMs. After the info is given to the AI, it can be extracted – even if code is written to suppress it. On the flip side, an absence of information can also be dangerous. Even though AI is a machine, it still holds biases, and these biases can encroach on liberty, run afoul of the laws, and even harm people.
Debbie encourages users to remember that it’s the AI developer’s profit and the user’s risk, so be diligent about the way you use it. Especially with the impending AI Act (which promises some of the stiffest legislative penalties), she prescribes leveraging AI and LLMs for low-risk use cases like summarizing content or drafting emails. In Debbie’s words, AI “is a source of information not a source of truth; you as the human have to bring the truth.”