AI Copilot Systems
With cutting-edge technologies like Large Language Models (LLMs) and Vector Databases, it is possible to have an AI Copilot system, an intelligent assistant designed to aid and recommend actions in performing tasks and making decisions. Just like a copilot in an aircraft assists the pilot, an AI Copilot system assists users in navigating and completing complex tasks.
Microsoft is a clear leader on this front as they are standardizing the architecture for AI Copilots. They have incorporated this technology into their products, including GitHub and MS Office 365. We aim to include AI Copilot systems in our clients’ internal products to improve their offerings, increase productivity, and reduce the time of information flow throughout their organization.
Generative AI in Incident Response Systems
One of our clients, a leading product and service provider in the manufacturing industry, is involved in complex and potentially hazardous operations. A top-of-the-class IR system will bring significant benefits to the organization, such as improved safety, data-driven decision-making, and improved operations and safety practices by reviewing and learning from past incidents and their resolution.
In addition to a state-of-the-art IR system, an AI Copilot enables conversational experience with the current and past state of the system, thus streamlining day-to-day processes for engineers. Technologies like LangChain provide powerful ways to connect LLMs to various data sources, including SQL and NoSQL databases, and create powerful knowledge bases.
By leveraging Generative AI, our client’s IR system can recommend actions to engineers to resolve an incident based on responses to past incidents, predict the likely outcomes from different actions, and help them prioritize responses to multiple incidents. The IR system will include interactive dashboards that provide real-time information about incidents and will be voice-command powered, thus making them more user-friendly and efficient.
IR system will be able to analyze data from various IoT devices used in the railway industry, providing a comprehensive picture of an incident. In addition, it will personalize the user experience by learning individual users’ preferences and adapting its interaction style accordingly.
We will be able to connect complex data sources to knowledge bases that LLMs can query by using image-to-text models to describe the content of images and pair them with Computer Vision models to provide an in-depth analysis of incident images. Similarly, we plan on using video classification models to detect issues and accidents in real time and pair them with transcription models to get text data out of videos into vector databases.
Generative AI is not only a buzzword; it provides powerful tools to transform IR systems. With the integration of GenAI, engineers will streamline their response to minor incidents and major accidents and learn from past occurrences. These tools will transform IR systems into an interactive, personalized platform where engineers can find relevant information instantly, providing data-driven insights and recommendations on resolving incidents.