Selected Projects
AI
Enhancing Efficiency Through AI
An innovative SME focused on improving operational efficiency through AI.
Challenge: The client required enhancements in reporting speed and quality while seeking to reduce repetitive tasks through AI.
Solution: We are actively working with the client, introducing their team to advanced prompting techniques with Chat GPT. This includes hands-on training to leverage generative AI for more efficient report generation, data analysis, and task automation.
Outcome: The project is ongoing, with continuous improvements being made as the team gains expertise.
AI
PolicyPulse: AI agents that identify, track and analyze legislation, regulation and policies
We are developing AI agents to identify, track and analyze legislation, regulation and policies, offering users a cost-effective solution to stay informed and be proactive. This project is being developed together with partners with deep legal and policy expertise.
Our first specialized AI agent is called “ECJ Today”, which is currently in the testing phase. The tool focuses exclusively on the jurisprudence of the Court of Justice of the European Union (CJEU). Our second specialized agent is “Topic Monitor”, which will launch in beta testing in May 2024. We are developing further specialized agents as part of an ecosystem of agents that, together with a coordinating “conversable agent”, will form the core of PolicyPulse.
Subscribe to ECJ Today here: www.lyfx.ai/ecj-today
Advanced Data Analysis
Advanced Analysis Supports Pioneers in Poultry Health Management
Our client developed a pioneering product that revolutionizes animal gut health without the use of antibiotics. They approached us for a detailed, independent analysis of their data focusing on the successful removal of an unwanted pathogen from infected animals. The data set included nearly 7,000 independent measurements from several trials.
Challenge: The challenge was multifaceted: not only to provide independent validation of the already proven efficacy of this innovative non-antibiotic solution but also to unravel its mechanism of action across different environmental conditions. The complexity of the data, coupled with the imperative of providing statistically sound evidence of the product’s effect, demanded an approach that went beyond conventional methodologies.
Solution: To navigate these complexities, we employed a sophisticated blend of advanced statistical analysis and probabilistic modelling. This choice was driven by the need to unearth patterns not readily visible through traditional means, particularly in understanding how the product performed in high-risk scenarios relating to pathogen concentrations. This methodological approach allowed us to validate the product’s efficacy.
Outcome: Our analysis led to groundbreaking findings. Our analysis showed that the product was exceptionally effective, especially in environments with heightened infection risks. A novel insight from our probabilistic approach evidence showing significant reduction in the presence of the pathogen in treated birds critically when assessing infections at the limits of detection using standard biological counting methods – a critical factor often overlooked in standard analyses. These birds showed a markedly lower prevalence suggesting that the product could significantly mitigate environmental contamination risks.
This case study underscores lyfX.ai’s dedication to leveraging advanced analytics to solve real-world problems. Our collaborative yet independent analysis has shown a path for future trials and product enhancements. By pinpointing the product’s efficacy and its potential for broader application, we have contributed to advancing a safer and more sustainable approach to poultry health management.
AI
Harnessing Neural Networks for Targeted Sales Pitch
Confronted with a diverse and extensive agricultural dataset, our goal was to explore the link between various soil types and the performance of an agricultural product.
We began with standard statistical analysis to detect correlations, but this method did not reveal any significant patterns. Undeterred, we turned to a more advanced approach, employing neural networks. This technique proved effective, allowing us to identify important trends based on a few parameters that were not obvious at first glance.
Armed with these findings, we conceptualized an user-friendly, web-based tool for the client’s sales force. Designed to input eight key parameters, this tool could predict the likelihood of product success with a high accuracy rate.
