Preventing Costly IT Outages before they escalated with Predictive AI and Streamlined Incident Diagnosis
Overview
A leading managed services provider (MSP) supports mission-critical IT operations for large clients under strict SLA agreements. Any major incident (MI) could result in substantial financial penalties and reputational risk. Their existing ServiceNow-based workflow lacked predictive capabilities and struggled to surface early warnings or consistent root cause insights across fragmented systems.
The Challenge:
The MSP needed a solution that could:
- Predict major IT incidents
- Identify root causes by surfacing patterns across disconnected alert and incident systems
- Improve data quality across service logs and classifications
- Integrate insights seamlessly into operational workflows
The Solution:
Evolution Analytics partnered with the MSP to design and implement an AI-powered incident prediction and diagnosis system. The solution included:
- Predictive machine learning models that analyze incident and alert data to forecast major outages
- Large Language Models (LLMs) to enrich and reclassify ambiguous or unstructured tickets, replacing low-value “other” categorizations
- An incident diagnosis application that consolidates telemetry data and uses LLMs to summarize probable root causes and recommend next actions
- Streamlit dashboards for case triage and model performance monitoring, embedded within the Snowflake environment
Technology Stack:
The entire solution was built leveraging the power of the Snowflake platform. Specific features include:
- Snowflake Database for centralized storage and scalable data processing
- Snowflake Notebooks for collaborative model development and training
- Snowflake Cortex for AI/LLM capabilities and Snowpark for ML processing
- Model Registry to manage and serve trained models in production
- Streamlit for interactive front-end applications
- Python for all procedures, modeling, and orchestration logic
Business Impact:
- Tripled the number of recognizable MIs by reprocessing historical data with AI enrichment
- Enabled proactive intervention for incidents predicted to become MIs, significantly reducing SLA penalties
- Accelerated root cause analysis, improving both response times and issue resolution accuracy
- Delivered unified visibility across data silos to enhance both technical and business decision-making
- Empowered the client’s internal data science team by making their models production-ready and scalable
Why It Worked:
EA’s unique combination of business insight, data science and engineering expertise, and deep knowledge of the Snowflake ecosystem made this project a success—delivering measurable improvements in both performance and client satisfaction.
Quote:
“Evolution Analytics didn’t just help us predict outages—they gave us the tools to prevent them. The integration of AI into our operations has fundamentally changed how we serve our clients.” — VP of IT Operations, Leading MSP
