See the full report—Incident metrics pulse: How organizations are measuring their incident management What metrics do you look at to measure how efficient your incident response is? This is a question we get asked all the time and one we empathize with deeply. While there are several well-established incident metrics that organizations commonly use, like MTTR and raw counts of incidents, a vast number of them are ineffective, or worse still entirely misleading.

Imagine an AI assistant that could automatically surface a whole host of useful incident response data points with just a prompt. Well, you won't need to imagine for much longer. That's exactly what we built in Assistant, one of our newest features powered by AI. In this episode, you'll hear from Charlie, the project lead for Assistant, to get a peek behind this game-changing product.

Incident summaries are the source of truth for responders joining an incident at any point. But the reality is that with so many things happening at once—like needing to respond to the actual incident—updating these summaries can fall by the wayside. Enter, Suggested Summaries, one of our newest features powered by AI. In this episode, you'll hear from Milly, the project lead for Suggested Summaries, to get a peek behind the curtain of this game-changing feature.

It's fair to say that effectively managing an on-call rota is crucial for ensuring the 'round-the-clock availability of your services. But it's more than that. Spending the time getting your rotas right also empowers and protects the folks who make it all possible: your team. Some best practices for doing this include using software to automate scheduling, setting up teams with clearly defined responsibilities, establishing escalation policies, and defining time limits for issue resolution.

Asking engineers to be on-call is usually a tough sell. Think about it: if someone asked you to add even more to your already packed workload, that would be a difficult proposition to say yes to. And that’s before you mention that this work typically happens late into the day and even (some) sleepless nights. Companies need to have an on-call function to keep their services and products running smoothly—it’s practically a non-negotiable at this point.

For financial services companies, good incident management is absolutely critical—maybe more so than in other industries. So, for Michael Cullum and his team at Bud Financial, the choice to build an incident response tool felt right for them in the moment. But very quickly, Michael and the team came face-to-face with the myriad limitations that come with building your own response tooling.

Recently we went live with one of our biggest product launches to date AI. And this product was unique in that it was broken up into four smaller projects: So naturally most folks might be wondering: What were the biggest differences between these projects and what went into actually building out each of these features? In this episode, you'll hear from Rob and Isaac, both Product Engineers who played a really critical role in the building out of related incidents, to get a peek behind the curtain.

With the world still working out the limits of LLMs and ever more powerful models being released each month, it’s a little hard to know where to begin. Whether it’s summarising and generating text, building a useful chat assistant, or comparing the relatedness of strings with embeddings, almost all of this now can be done via a few simple API calls. It has never been easier to incorporate these new technologies into your own product.

Like most things, our AI Assistant started out as an idea. One of our data scientists, Ed, was working with our customers to improve our existing insights. But the most common theme that kept surfacing was the wide-range of use cases that our customers wanted to use insights for. Using this user feedback as our inspiration, we came up with the idea of a natural language assistant that you can use to explore your incident data.

This week was a particularly exciting one for us at incident.io. We launched not one, not two, but four AI-powered features to help folks get the most out of their incidents. In this episode of The Debrief, we sit down with Ed Dean, Product Analyst, and Charlie Revett, Product Engineer, to talk through all of these features and discuss how they're already making a measurable impact. You'll also hear them talk about: You can learn more about our AI features here.