The client wanted to use machine learning to predict the performance of HVAC system components in order to reduce energy consumption and optimize system performance. Optimization of a whole building’s HVAC system based on model predictions has not been carried out before, to the best of our knowledge. The challenge was to build a robust machine learning solution that integrates with the client’s wider Microsoft-based technology stack and can be retrained on new data as it becomes available to maintain performance.
To ensure reliability and in line with MLOps best practices, ProCogia built a microservice that comprises an automated data ingestion pipeline, training pipelines for advanced machine learning models and a monitoring system with appropriate performance metrics.
To mitigate the risk of data drift and improve prediction accuracy, models are configured to be regularly retrained on new data as it becomes available.
We developed advanced machine learning algorithms and enhanced their performance through feature engineering, drawing from diverse data.
We implemented robust cross-validation specific to time-series models and used this to inform model selection based on historical data, further improving accuracy.
ProCogia were able to deliver a production-grade microservice, ensuring reliable and accurate predictions with seamless integration with the client’s existing software application.
Our solution helped the client gain a competitive edge as the only provider of HVAC optimization with enhancement from predictive machine learning
The client wanted to use generative AI to develop a chatbot that was able to offer guidance to patients with chronic disease to improve public health outcomes. The challenge was to build a capable, sensitive and ethical chatbot that could choose the best way to interact with users whilst prioritizing patient safety and responsible AI principles.
To ensure an ethical and responsible solution that guards against offering potentially harmful advice to patients, ProCogia built a chatbot that was able to use context from medical guidance documents to ensure accuracy. We used a “red teaming” approach to identify vulnerabilities to misinformation, and acted on the findings to further enhance security.
We implemented advanced generative AI algorithms and enhanced their performance through prompt engineering and finetuning, drawing from diverse data and the use of state-of-the-art image analysis methods.
To ensure privacy, we deployed the chatbot to a secure infrastructure that encrypts all user data.
We built a scalable, production quality solution with containerization, CI/CD and a modular, extensible and well-documented codebase. A web-based frontend and a WhatsApp access channel were provided to allow as many patients as possible to access community health information.
ProCogia were able to deliver a production-grade chatbot, able to sensitively respond to different users depending on their assessed profile with seamless integration with the client’s contextual data.
Our commitment to responsible AI principles and governance gave the client confidence that we could be trusted with handling sensitive patient data and develop a solution that prioritized safety.
We adopted best practices to build a scalable solution that is robust, sustainable and cost-effective.
Evaluating Generative AI models Evaluating the performance of Generative AI models, particularly in applications such as chatbots, presents a unique
Evaluating Generative AI models Evaluating the performance of Generative AI models, particularly in applications such as chatbots, presents a unique
