For a lot of teams, incident management can be a bit of a headache. It's stressful. It's not optimized. The whole process can feel like it's being held together with tape. Worst of all? Responders are the ones feeling the brunt of it. But in reality, your customers are, too. Think about it: But honestly, the situation doesn't even have to be so dire. Things can be, generally speaking, totally fine. But you recognize that there are some things that you can do to make incident response really shine at your organization.

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. You'll hear him chat about.

In this clip of The Debrief, Charlie explains exactly what teams can look forward to getting from Assistant. 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.

In this clip of The Debrief, Charlie explains how we determined what "good" would look like during the process of building out Assistant. 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 clip of The Debrief, Charlie explains why design partners, and the feedback they gave, was so crucial to the product we ended up building. 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 clip of The Debrief, Charlie explains why prompt engineering can be so challenging. 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.

In this clip of The Debrief, Milly walk through how the incident.io team actually built out our latest AI feature: Suggested Summaries 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 clip of The Debrief, Milly explains why incident responders don't update summaries as frequently as they should. 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 clip of The Debrief, Milly explains the challenges of prompt engineering. 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 clip of The Debrief, Milly explains what Suggested Summaries are and how they can be a huge benefit for teams. 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?