At 8am on a typical Tuesday, Sarah, an experienced incident commander, faces a familiar grind: dozens of new tickets stacked up overnight. Some are critical—database connectivity issues threatening core trading systems, authentication failures locking out financial advisers, or data pipeline alerts jeopardising market data feeds. Others are routine service requests, misclassified but still clogging the queue. Sarah spends the next two hours sorting, prioritising, routing, and searching documentation before her engineers can even begin solving problems.
This has been the reality of IT operations for decades: highly skilled people consumed by repetitive tasks rather than building system resilience.
But it doesn’t have to be this way.
How Firemind reimagined incident management in Managed AI
Using Firemind Pulse™ platform, we built a set of specialised AI agents to automate the repetitive, error-prone parts of the incident management process.
When Sarah logs in today, her morning looks very different. Before she even reviews the queue, AI agents have already:
- Reclassified tickets (ensuring critical incidents aren’t misfiled as service requests)
- Adjusted priorities in line with business impact and SLAs
- Identified the right technical components and mapped them to the appropriate engineering roles
- Summarised knowledge and past incident resolutions so the team doesn’t waste time digging
- Suggested initial troubleshooting steps based on historical patterns
What once took hours is now completed in minutes. In high-volume environments, this translates to 30+ hours of effort saved per week—time incident commanders and engineers can now redirect towards higher-value resilience work.
Firemind Pulse™ and technology under the bonnet
Our architecture is AWS-native, ensuring scalability, security, and compliance from day one. The solution integrates:
- Amazon Bedrock for LLM orchestration
- AWS Lambda and Step Functions for event-driven workflows
- Amazon DynamoDB for storing AI agent outputs
- Freshservice APIs to automate ticket updates and routing
This isn’t just a demo—it’s enterprise-ready automation with measurable impact.
Reliability through continuous evaluation
For CIOs and Heads of AI, trust is as important as speed. That’s why we embed continuous evaluation into every AI workload:
- Monitoring accuracy, hallucinations, and harmful content in real time
- Tracking deviations against agreed performance baselines
- Displaying results in live Quicksight dashboards for transparency
- Alerting teams to anomalies before they impact operations
This evaluation framework means our clients can scale AI with confidence, aligning automation with both business continuity and regulatory requirements.
The human advantage
Automation doesn’t replace Sarah. Instead, it frees her from repetitive triage so she can focus on critical decision-making and proactive system resilience. Humans remain firmly in control, with AI acting as a trusted co-pilot.
Lessons for enterprise leaders
From our journey, a few principles stand out for IT leaders considering AI-driven operations:
- Start with high-volume, repetitive pain points—incident management is often the most obvious win.
- Document processes before automating to ensure AI learns from clean, structured workflows.
- Set SLA-aligned accuracy targets—99.9% for financial systems versus lower thresholds for routine tasks.
- Keep humans in the loop for escalation and complex problem-solving.
- Invest in monitoring from the outset—AI without oversight is a liability.
Looking ahead
Today, our clients are already seeing the benefits: faster ticket resolution, reduced mean time to resolution (MTTR), and measurable gains in service continuity. Tomorrow, the shift will be even bigger: from reactive firefighting to predictive, self-healing IT operations.
At Firemind, we don’t just build AI for our clients—we use it ourselves, proving its value in production before bringing it to market. With over 200 AI projects delivered, including complex workflows in financial services, insurance, and mortgage processing, we’ve seen first-hand how automation scales across industries.
The future of IT operations isn’t ticket triage—it’s AI-driven resilience. And that future is already here.
