The first tool I add after going live
The best AI-first tools do not add complexity. They remove the work between question, insight, and action.
Before launch, during builds, the focus is mainly:
Does this behave as per the spec?
Does it fulfill the vision?
If one answer is no, we iterate.
After launch, the objective changes.
It becomes critical to understand how users actually interact with the product, where they get stuck, and where the experience can be improved.
For BetterShare, we gave PostHog a try.
I expected the boring analytics and monitoring layer, and was surprised by how AI-native the product feels.
Beyond dashboards, error monitoring, session replays, surveys, feature flags, and the rest of the usual product analytics stack, PostHog is also easy to set up, either through wizards or prepared instructions for your favorite AI agent.
But they go further.
Inside the portal, you can literally talk to their agent and get useful work done: understand an error, search your data, create custom dashboards, write queries, and surface exactly what you need without getting lost in menus.
The Product Manager loved it because she could extract insights on her own without waiting on development.
I loved it because it helped surface errors and reduced the friction of briefing coding agents.
Two lessons for founders:
First, after launch, you should not need five tools, three dashboards, and a data analyst just to understand where your users are getting stuck.
Second, if you are adding AI to your own product, make sure it removes real complexity for the user. Not “AI inside the product” as a gimmick.
AI that helps someone get from question to answer, from error to action, and from data to decision faster.
That is where AI-first tools actually become useful.
If you need help building the right way, without wasting time on the wrong setup, let’s talk.