Rahul Bhavsar

Our AI employees have names. Peter is an SDR, Kavya sells insurance, Arjun is a loan officer, Priya handles renewals.

We did it because customers found it easier to talk about "Peter's pipeline" than "the lead qualification workflow." Teams started treating the agents like colleagues with a job, which made it easier to define what each one owns.

A brand might appear regularly in ChatGPT recommendations while showing up less frequently in Gemini, Perplexity, or Google AI Mode.

Each platform uses its own retrieval systems, available sources, and methods for generating answers. As a result, the same question can produce different brand recommendations depending on where someone asks it.

Dashboards are loud. The signals that matter are usually quiet, and I'd like to collect yours.

I've written here before about how our most active users at Murror turned out not to be our happiest ones. That changed how I read numbers. Sessions, streaks and retention curves all measure that people came back. None of them tell you whether coming back was good for them.

Since then I've been more interested in small, quiet signals that are hard to put on a chart. Things like a user who stops using your product because they no longer need it. Someone who writes in to correct a detail, which means they read closely. A person who recommends it with a caveat, because they're being honest about who it is and isn't for.

My own rule of thumb is that a signal is worth trusting when it would still look good if you were wrong about your product. A big engagement number can rise because people love something, or because it nags them. A quiet signal like "they told a friend, unprompted" is harder to fake, even by accident.