How We Built an AI That Never Forgets Production Incidents

Mathan kumar — Sat, 04 Jul 2026 19:20:55 +0000

How We Built an AI That Never Forgets Production Incidents

Can AI become your smartest Site Reliability Engineer? We decided to find out.

Every software engineer has experienced that one stressful night. You're finally asleep when your phone suddenly buzzes. It's 2:47 AM. PagerDuty has triggered an alert, CPU usage is skyrocketing, users are reporting errors, and Slack is already filling up with messages. One engineer is checking dashboards, another is digging through Kubernetes logs, while someone else is asking, "Did anyone deploy something recently?" Meanwhile, every passing minute means more downtime, frustrated customers, and lost revenue.

For years, we've accepted this chaos as part of running software in production. But the more incidents we handled, the more one question kept coming back to us:

Why are engineers spending more time finding the problem than actually solving it?

That simple question became the inspiration behind Incident AI.

The Problem with Modern Incident Response

Modern cloud infrastructure is incredibly powerful, but it's also incredibly complex. Today's applications aren't built as a single service anymore. They're made up of hundreds of interconnected microservices, Kubernetes clusters, serverless functions, databases, message queues, APIs, and CI/CD pipelines. Every component depends on another, creating a massive web of dependencies.

When something breaks, engineers don't receive one clear alert explaining what happened. Instead, monitoring tools flood them with hundreds of notifications from different services. The real root cause is buried beneath a mountain of symptoms. Teams spend valuable time switching between dashboards, reading logs, comparing metrics, and trying to understand which alert actually matters. Traditional monitoring tools are excellent at telling us that something is broken, but they rarely explain why it happened.

Why We Built Incident AI

We didn't want to build another dashboard. There are already plenty of monitoring platforms that visualize metrics and alerts. What engineers actually need is something that understands those alerts, connects the dots automatically, and explains what's really happening.

That's exactly what Incident AI was designed to do.

Instead of simply displaying infrastructure data, Incident AI continuously analyzes logs, metrics, traces, deployment history, and infrastructure events. Within seconds, it identifies the most likely root cause, estimates the business impact, and even recommends actionable fixes. Our goal was to create an AI-powered Incident Commander that feels like having your most experienced Site Reliability Engineer available 24 hours a day.

Teaching AI to Think Like an SRE

One of the biggest challenges during an incident isn't collecting information—it's making sense of it. Experienced SREs instinctively connect unusual CPU spikes with slow database queries or identify that a frontend issue actually started with a backend dependency. We wanted our AI to develop the same reasoning process.

Incident AI begins by collecting telemetry from across the entire infrastructure. It examines application logs, stack traces, Kubernetes events, performance metrics, deployment history, and distributed traces simultaneously. Instead of treating every alert separately, it correlates all of this information to build a complete picture of the incident.

The result isn't just another alert. Engineers receive a detailed root-cause analysis, confidence score, estimated business impact, suggested remediation steps, and even executable commands they can use immediately.

The Biggest Problem Isn't Downtime—It's Forgetting

While building Incident AI, we realized something surprising. Downtime wasn't always the biggest problem.

Memory was.

Every engineering team has experienced this. A senior engineer spends hours solving a difficult production issue. The incident gets resolved, everyone moves on, and eventually the knowledge disappears. Months later, another engineer encounters the exact same problem, but nobody remembers how it was fixed before. The investigation starts from scratch all over again.

That seemed completely unnecessary.

We asked ourselves a different question:

What if every production incident became permanent organizational knowledge?

Giving Production Incidents a Memory

This idea became one of the core features of Incident AI.

Whenever an incident is resolved, the platform doesn't simply close the ticket. Instead, it captures everything that happened—the telemetry, logs, metrics, identified root cause, and successful remediation steps. Using semantic search powered by Retrieval-Augmented Generation (RAG), every incident becomes searchable knowledge.

The next time a similar issue appears, Incident AI doesn't start its investigation from zero. It recognizes similar patterns from previous incidents and immediately surfaces proven solutions. Instead of relying on someone's memory, the organization builds a permanent knowledge base that grows smarter with every production incident.

Why Speed Matters

During a critical production outage, waiting even a few extra seconds feels like an eternity. Most AI-powered tools generate impressive responses, but they often take too long to be useful in real-world incident response.

That's why we built Incident AI using Groq LPUs running Llama 3.3 70B. This allows the platform to process large amounts of telemetry data and generate meaningful diagnostic reasoning almost instantly. Instead of waiting tens of seconds for AI to respond, engineers receive insights while the incident is still unfolding, helping them reduce downtime and restore services much faster.

Understanding the Blast Radius

Production failures rarely remain isolated. A database outage can quickly cascade into authentication failures, API timeouts, frontend errors, and eventually failed customer checkouts. By the time users notice the issue, the original root cause may already be hidden beneath dozens of secondary failures.

Incident AI automatically maps these service dependencies and visualizes the blast radius of an incident. Engineers can immediately see not only what has already failed, but also which systems are most likely to fail next. This makes it much easier to prioritize responses before the outage spreads further across the infrastructure.

Bringing Everything Together

One of the most frustrating parts of incident response is constantly switching between tools. Engineers jump from Datadog to Prometheus, CloudWatch to PagerDuty, Slack to GitHub, trying to collect enough information to understand what's happening.

Incident AI removes this constant context switching by bringing all of these signals into a single intelligent workflow. Instead of manually piecing together the story from different platforms, engineers receive a unified view of the entire incident along with AI-powered reasoning that explains what actually matters.

The Technology Behind Incident AI

Building a platform capable of real-time incident analysis required technologies that prioritize both speed and scalability. We chose Next.js 15 and TypeScript for the frontend to create a fast, modern user experience, while Framer Motion powers smooth interactions and animations.

On the backend, Supabase, PostgreSQL, and pgvector provide reliable data storage and semantic search capabilities. For AI inference, we integrated Groq LPUs with Llama 3.3 70B, while Retrieval-Augmented Generation (RAG) allows the platform to remember and retrieve historical incidents with remarkable accuracy.

Our Vision

Our goal isn't simply to build another observability platform.

We want to fundamentally change how engineering teams respond to incidents.

Today's monitoring systems tell us that something is broken.

Tomorrow's systems should explain why it's broken.

Eventually, they should fix the problem before customers even notice it.

We believe AI won't replace Site Reliability Engineers. Instead, it will eliminate repetitive investigation work so engineers can spend their time designing better systems, improving reliability, and building new features instead of constantly firefighting production issues.

Final Thoughts

Every production incident teaches valuable lessons. Unfortunately, most organizations lose those lessons over time as people change teams, documentation becomes outdated, and experience disappears.

With Incident AI, we wanted to build a platform that never forgets.

Every outage becomes knowledge.

Every investigation makes the system smarter.

Every resolved incident helps solve the next one faster.

Because in the future, the best incident response platform won't just monitor your infrastructure.

It will continuously learn from it.

DEV Community: Mathan kumar

How We Built an AI That Never Forgets Production Incidents

Can AI become your smartest Site Reliability Engineer? We decided to find out.