Driving Safety Insights with Snowflake Cortex AI
About this project:
Transforming Safety Data Management with Snowflake Cortex AI & Automated Machine Learning
"Eden Smith partnered with a global organisation to modernise safety data management. By combining Snowflake’s data cloud, Cortex AI, and intuitive dashboards, we delivered a scalable solution that automates risk detection, enhances accessibility, and empowers decision-making - helping the business move from reactive reporting to proactive safety intelligence."
Nicholas Deveney
Group Head of Data & Consulting
Executive Summary
Scalable, Automated Solution for Real Time Analysis for Safety Data Management
Challenge: The organisation struggled with fragmented systems, manual reporting, and limited ability to detect safety risks from complex, growing datasets. They required a scalable, automated solution for real-time analysis and user-friendly access to insights.
Solution: Eden Smith built a robust Snowflake-based architecture, integrating machine learning workflows, Cortex AI, and interactive dashboards to streamline risk analysis and enable natural language interaction with data.
Results:
The business gained proactive risk identification, real-time dashboards, and AI-driven data exploration - transforming safety management into a scalable, automated, and insight-driven function.
Challenge
Solution
Outcome
Our client faced a critical need to modernise their approach to safety data management and risk assessment. Existing systems struggled to handle a growing volume and complexity of both structured and unstructured data, limiting their ability to proactively identify safety risks and generate actionable insights.
Additionally, business users lacked intuitive access to data and had to rely heavily on manual reporting processes, which were time consuming and prone to error.
The organisation required a scalable, automated solution that could streamline data ingestion, transformation and analysis, while also enabling real-time decision making and reporting. The challenge was to design a robust architecture that could support advanced analytics and machine learning workflows, integrate seamlessly with existing tools and provide user friendly interfaces for both technical and non-technical stakeholders.
Critical Need to Identify Safety Risks
A Scalable Dataverse using Snowflake Data Cloud & Snowflake Cortex AI
Eden Smith delivered a comprehensive data and AI solution, leveraging Snowflake’s data cloud and Cortex AI capabilities. Best practice architecture was implemented comprising of Stage, Transform and Serve layers to manage data efficiently. This formed the foundation of a scalable dataverse that was tailored to the organisation’s needs.
An automated machine learning workflow was developed to assess daily operational activity for safety risks by preparing model features and data, training and tuning multiple models, testing, selecting, deploying models and providing comprehensive reporting.
The solution also included an Agentic AI component using Cortex Agent, Cortex Search and Cortex Analyst, deployed via Streamlit. This Enabled conversational data analysis, automating charting and generative document creation.
Business users were also empowered with intuitive dashboards built in Sigma and Tableau which provided real-time visibility into safety metrics and trends.
The impact of the project meant that the orgnaisation has a scalable solution that is tailored to their needs. The product works with complex structured and unstructured data. The automated reporting enables users to identify real-time safety risks and deliver actionable insights.
Key outcomes included:
- Successfully delivered a machine learning workflow that automates feature preparation, model training and deployment, enabling proactive safety risk identification.
- Enhanced data accessibility by providing business users with real-time dashboards.
- Deployed AI solutions allowing users to interact with data via natural language.
