Inspiration
When I was working with Anthropic Claude, I came across a problem: whenever the token expired, I had to move my entire workflow to another AI platform manually. That’s when an idea clicked in my mind — why not create a project that automatically switches the AI model for me instead of forcing me to move my whole project from one platform to another?
What it does
sentinel-ops ai is an autonomous ai infrastructure resilience platform that monitors ai providers in real time and automatically performs failover whenever a provider becomes unavailable.
the system continuously tracks provider health, latency, uptime, and failures. if the primary provider fails due to outages, quota limits, or high latency, sentinel-ops instantly reroutes requests to a fallback model running through ollama without interrupting the user experience.
it also provides a real-time observability dashboard with live provider monitoring, incident tracking, latency analytics, websocket event streaming, and failover visualization.
How we built it
we built sentinel-ops ai using a modern full-stack architecture focused on real-time monitoring and autonomous failover systems. the backend was developed using fastapi with asynchronous python services, websocket communication, health monitoring systems, circuit breaker patterns, and a custom failover engine that intelligently switches between ai providers. we integrated openai as the primary provider and ollama as the local fallback inference system.
the frontend was built using next.js, tailwind css, shadcn/ui, recharts, and framer motion to create a real-time observability dashboard inspired by modern infrastructure monitoring platforms like grafana and datadog.
we also implemented live provider status tracking, incident logging, latency analytics, websocket-based event streaming, and automatic recovery detection to simulate a production-grade ai infrastructure resilience platform.
Challenges we ran into
one of the biggest challenges we faced was building a reliable real time failover system between ai provider . then a major challenge was debugging the ollama integration, where the provider was repeatedly being marked as unhealthy even though the local inference server was running correctly. we had to deeply debug async health-check systems, provider communication logic, timeout behavior, and model compatibility issues before stabilizing the failover engine.
Accomplishments that we're proud of
we are proud that sentinel-ops ai successfully demonstrates autonomous ai provider failover in real time. the system can detect when the primary ai provider becomes unavailable and automatically reroute traffic to a fallback local model without interrupting the user experience.
What we learned
through building sentinel-ops ai, we learned how important resilience engineering and failover systems are becoming in modern ai infrastructure. we gained hands-on experience with asynchronous backend architecture, websocket communication, real-time monitoring systems, circuit breaker patterns, and ai inference orchestration.
What's next for SENTINAL OPS
our next goal is to evolve sentinel-ops ai into a fully production-ready ai infrastructure resilience platform capable of managing multiple ai providers at scale. in the future, we plan to add support for providers like anthropic, gemini, groq, and together ai while introducing intelligent traffic balancing, predictive outage detection, distributed failover systems, and kubernetes-native deployments.
Log in or sign up for Devpost to join the conversation.