Specsight
Observability is the unglamorous layer that separates an AI demo from an AI product that survives contact with real users. Specsight is going after exactly that gap, and it's a smart bet. Teams shipping AI are moving at a pace where they're often debugging blind, watching model logs and product metrics in two different tools, hoping the dots connect. A product observability layer that treats AI as a first-class citizen instead of an afterthought feels overdue.
I'd love to know how granular Specsight gets, whether it traces individual prompts through to user actions or flags regressions before they hit the dashboard. The hard part of AI observability is that failures tend to show up as gradual drifts in quality and tone, not clear-cut binary errors, so a good tool needs to catch those without drowning the team in noise. That's where the real value lives, and where the trust gets built.
The teams that ship fast with AI are exactly the ones who hit these walls early. If Specsight nails the workflow, it could become the kind of tool founders stop being able to live without.