Operations | Monitoring | ITSM | DevOps | Cloud

Documentation search is often the last thing devs think about, until someone posts publicly that they couldn't find a basic answer, or your support queue fills up with things that are genuinely in the docs. We decided to get ahead of that. This is the story of how we went from a minimal keyword-only search on our docs to a conversational Ask AI experience.

Part of the motivation for this post is how cloud API economics are shifting: Anthropic is moving large enterprise customers toward per-token, usage-based billing (unbundled from flat seat fees), which makes “always call the API” a moving cost line for teams at scale. A hybrid or local layer is one way to keep spend bounded while you still use premium models where they matter.

Everyone is using AI to help with post-mortems now. The pitch is obvious: post-mortems are time-consuming, the blank page is brutal, and AI is very good at producing structured, confident-sounding documents quickly. We're not here to push back on that. We've built AI into our own post-mortem experience, pulling your Slack thread, timeline, PRs, and custom fields together and giving your team a meaningful starting point in seconds. We think that's genuinely valuable, and the teams using it agree.

We've been building the broader incident.io platform for several years now, and one thing we've learned is that UX matters more here than almost anywhere else. When an incident fires, there's no room for poorly designed interfaces or fumbling through features you haven't touched in a while. The product has to be ergonomic: easy to pick up, easy to navigate, with the right things at your fingertips at exactly the right moment. We've put a lot of effort into this over the last 5 years.

Key takeaway: AI agents and RAG pipelines only reach production-grade accuracy when they are developed against byte-level clones of real production data. Without environment parity, the "repro gap" leads to inevitable AI failure.

SREs and platform teams are overwhelmed by the effort of manually troubleshooting ever-more complex cloud-native environments. This pain is driving a breakneck adoption of AI SRE solutions that promise to automate core reliability practices, from root cause analysis to capacity planning. For teams with strong engineering talent, creating a DIY AI SRE seems like a straightforward challenge.

Your LLM observability stack probably handles individual model calls well enough. Latency, token counts, error rates, maybe even evaluation scores....