The Challenge: Scaling Expert Analysis Across a World-Wide Fleet of Distributed Databases
Redis has built the world’s fastest data platform, trusted by enterprises globally to power mission-critical applications. As the backbone of caching, real-time analytics, and message brokering across every industry, reliability isn’t just a priority. It’s the foundation of Redis Cloud.
Redis Cloud delivers databases as a service across AWS, GCP, and Azure, operating a global distributed inventory of small “blast radiuses” that require constant monitoring and care. Maintaining such a distributed environment demands precise coordination and constant vigilance to keep mitigation times low and reliability high.
Joe Danford, SVP of Cloud Operations, describes his organization as the steady hands behind Redis Cloud, the team trusted to keep the service running at its best. They combine the urgency of firefighters with the discipline of engineers to ensure the global fleet of databases and clusters remains consistently available, fast, and resilient every hour of every day.
As Redis Cloud continues to scale, they’ve identified an opportunity to further enhance how real-time system signals are interpreted and acted on, ensuring they consistently deliver the speed, reliability, and customer experience their users expect, even as the platform grows.
"We wanted to take the team’s subject matter expertise and turn it into something actionable in seconds, not minutes. That's key to delivering the kind of high-touch reliability experience we expect for Redis Cloud customers."
For example, during periods of peak demand, Redis may proactively make adjustments to ensure throughput remains optimal. In the past, identifying the best action path could take up to 20 minutes. With AI-driven analysis, Redis now targets sub-minute insights, enabling faster optimization and continued innovation.
The stakes are inherently high whenever an operational action touches a customer workload, where maintaining trust requires speed paired with deliberate care. Danford recognized that the path forward wasn’t simply more automation, but scaling human expertise by capturing deep institutional knowledge and converting it into consistent, real-time decisions at cloud scale.
Why Generic AI Fell Short
Redis had built extensive internal monitors and metrics to detect deviations proactively, managing large-scale multi-database fleets with careful automation and scripting.
As AI capabilities emerged, Redis began exploring how LLMs could analyze production information in near real time. The potential was clear: combine improved automation with AI to deliver faster, more consistent insights into production issues. But as the team evaluated solutions, they encountered a fundamental problem with most AI vendors: insights without action.
Redis needed more than summarization. They needed a system that could think like their best engineers: understanding context, executing troubleshooting playbooks, and providing definitive assessments with specific next steps.
"Most vendors we saw just gave 'insights', generic LLM summaries that weren't actionable. What we wanted was a tool that could act like an engineer: precise, consistent, and actionable. It needed to learn from our subject matter expertise and guide next steps automatically. That's where Wild Moose differentiated itself."
The “Secret Sauce”: Turning In-House Expertise Into Executable Playbooks
Wild Moose stood out for its ability to do something fundamentally different: transform Redis's internal expertise into production-ready AI that could execute like an engineer.
The platform integrated with Redis’s observability stack (metrics, logs, and alerts), providing real-time visibility into the production environment. By ingesting Redis’s documentation, context, and subject matter expertise, it executed targeted playbooks for issues such as low memory or high traffic patterns, delivering precise, actionable guidance rather than vague suggestions.
"The real 'secret sauce' was the knowledge transfer. Wild Moose could take our Redis expertise, documentation, and context, then execute playbooks for specific conditions like low memory or high traffic. When I saw the first outputs, it was an 'oh wow' moment. It was the first time I believed an AI tool could actually support real production workflows, not just summarize issues."
For the first time, Danford saw an AI tool capable of running in production at the speed and precision Redis demanded. The platform’s flexibility aligned perfectly with Redis’s goals: reduce mitigation time, improve visibility, enhance the high-touch customer experience that defines Redis Cloud, and provide more meaningful, data-driven feedback to Product and R&D so improvements can be delivered faster.
The Results: Faster Response, Smarter Operations, Happier Engineers
Today, Wild Moose is helping Redis automate critical parts of its operational workflow. The platform analyzes logs and metrics across Redis Cloud to surface definitive root cause assessments rather than vague correlations, producing triage responses that are clear, contextual, and trustworthy.
That level of specificity transforms how Redis operates. Instead of scrolling through dashboards and logs to find contributing factors, engineers can jump straight to resolution, often just reviewing and approving Moose’s recommended fix within seconds.
“We expect to reach 20–50–80% AI-assisted resolutions over time,” says Danford. “The early impact has already shown strong value in reducing investigation time and providing actionable insights.”
For Redis engineers, the impact is tangible. Alerts that once required multiple people and lengthy investigation cycles now resolve with a single AI-verified recommendation. Consistency has improved, and confidence is higher across the board.
“It’s about giving engineers confidence. Wild Moose analyzes logs and metrics to deliver definitive assessments, not vague suggestions. That’s a huge shift in how we work.”
By automating first-response logic, the platform frees engineers to focus on solving and innovating rather than diagnosing. And in an environment managing thousands of distributed clusters, even small efficiency gains compound into significant operational improvements.
Redis’s rollout has shown measurable improvements:
- Faster investigations: Average diagnostic time for key scenarios dropped from 20 minutes to less than one.
- Higher accuracy: Wild Moose’s recommendations match Redis engineers’ conclusions with >90% accuracy.
- Improved engineer experience: On-call engineers spend less time chasing symptoms and more time improving systems.
These outcomes validate Redis’s original hypothesis: AI enhances reliability not by guessing, but by scaling human expertise through automation. Wild Moose serves as a force multiplier, handling triage and analysis at scale so Redis engineers can focus on what they do best: building and improving the world’s fastest data platform.
What’s Next: AI-Driven Operations as the New Standard
For a company committed to being the world's fastest data platform, speed is an operational imperative. Wild Moose has become a core part of how Redis delivers on that promise, transforming expertise into action and ensuring that operational excellence scales with business growth.
As coverage expands and AI-assisted resolutions increase, the CloudOps team is gaining capacity to focus on strategic improvements while maintaining the reliability customers expect.
"For Redis, Wild Moose has been a no-brainer. It's accelerating our outcomes and making us faster, which is critical for maintaining the fastest data platform in the world."
Looking ahead, Redis envisions Wild Moose evolving into an autonomous AI agent that functions as an integrated member of the CloudOps team.
This vision reflects a broader shift in how leading operations teams think about AI: success isn’t about replacing human expertise, but scaling it. AI is enabling small teams to maintain reliability across massive, distributed environments that would otherwise demand significantly larger headcounts.
"I see Wild Moose as a true team member. Eventually, it could act as a Tier 1-3 engineer monitoring Redis Cloud 24/7. It won't replace people but will supercharge them, handling investigations so engineers can focus on mitigation and continuous improvement."
Still, as Danford emphasizes, achieving that vision requires more than the right technology. It calls for cultural change, trust-building, and a willingness to rethink how humans and AI collaborate.
Redis’s journey illustrates what the future of operations looks like: a model where AI amplifies human capability, empowering teams to deliver speed and reliability at a scale once thought impossible.
