In the software development space, RAG solutions are being used to enhance productivity and streamline processes. By indexing and searching the entire codebase of a project, these solutions provide relevant, context-aware results from an LLM. Instead of simply suggesting code snippets, RAG-based tools can analyze and generate entire use case implementations across multiple programming languages. The result is a significant boost in productivity, enabling software teams to work faster and more efficiently.
RAG Architecture Consulting for Enterprise AI Applications
Home→Search ResultsEnterprise RAG Architecture Consulting Services Keyhole’s RAG architecture consulting services help enterprise teams connect private data to AI applications so they can improve answer quality, keep responses current, and deploy AI within existing security and system requirements. Keyhole Software provides RAG architecture consulting and implementation services for enterprise organizations looking to integrate LLM-powered applications into real systems. We design …
.NET in the Cloud: How to Leverage AWS/Azure for Scalable Solutions
Attention: This article was published over 2 years ago, and the information provided may be aged or outdated. While some topics are evergreen, technology moves fast, so please keep that in mind as you read the post.As enterprises increasingly adopt cloud technologies, the importance of scalable and efficient solutions like Microsoft’s .NET has never been more evident. This platform, compatible …
Angular Material Drag and Drop – Strengths and Limitations
When Drag and Drop was introduced to the Angular Material/CDK in version 7, it promised to support free dragging, interactive lists, and other common drag and drop operations without third-party library dependencies. Since that initial release, it has received consistent updates to further that goal.
In this blog post, I will be exploring some of the strengths and limitations of the Module that I encountered while implementing both simple and complex drag and drop functionality with CDK version 13.3.5.
Leveraging Docker to Quickly Setup an Object Detection API
In this blog, we utilize the strengths of Docker containers to quickly spin up two separate containers that we can utilize for our software development needs – one running the DeepStack API software and the other running a utility to help us get started with the DeepStack API.
The best part is that once we are comfortable with our setup, we could quickly and easily stop and remove the DeepStackUI utility container to free up resources all while continuing to run the DeepStack API software without interruption.




