Building higher-touch AI products that are applicable across the entire customer base of an existing enterprise SaaS provider on release does feel impossible.
It's very obvious when you look at the AI companies that have 'exit-velocity' in this space that they are playing in that they are either one of the following:
1. Targeting a need within that org that's narrow enough to be MVP-able enough across a diverse customer base
2. Targeting IC work directly
As we add more complexity here, it's going to be a long, long slog to market - even with model advancements.
We recently released our On-call product, and as part of that, had to think a lot about redundancy and 'failing safety'.
Here's how we achieve it - and how we're thinking about it. Interested if any other examples of this exist in the wild - I'd love to know more about how eg: Datadog achieve this.
We recently released our On-call product, and as part of that, had to build a mobile app.
We'd noticed there's been some debate recently regarding React Native, and if you can build 'native tier' apps using it. We'd seen a lot of this but as part of an initial hackathon tried Explo / RN / Nativewind and were completely blown away by both the quality of app we could produce and the developer experience that let us do this quickly.
If anyone's in this space / thinking about making a technical decision here I couldn't recommend this stack strongly enough!
It's very obvious when you look at the AI companies that have 'exit-velocity' in this space that they are playing in that they are either one of the following:
1. Targeting a need within that org that's narrow enough to be MVP-able enough across a diverse customer base
2. Targeting IC work directly
As we add more complexity here, it's going to be a long, long slog to market - even with model advancements.