The hive mind for your product | Rover by rtrvr.ai

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I watched Pluribus in one sitting, and one idea would not let go — and it is not the virus.

Everyone knows Carol. Her history, her tastes, what she is going through right now — and she never had to tell anyone. She dials any number and whoever answers has the full context and just does the thing. No ticket, no transfer. Twelve immune holdouts on the planet, each served like the only person on it, because behind every conversation stands the memory of billions.

In the finale, Carlos warns her: "These weirdos have eyes up above. They can read lips from space."

Creepy. Also the service every company promises its customers and none of us delivers.

We do not know our customers

Not for lack of data — we drown in it. We lack one mind. What a company knows about a single customer is shattered across a sales rep, an account manager, a CSM, a support queue, and six dashboards that do not talk. Almost none of it reaches the customer in the moment.

I spent years in post-sales at Google, on Ad Manager's biggest accounts. Know an account cold and you see the problem before the customer finishes describing it. That knowing was the entire value of me — and it walked out of the building every evening in one head.

So companies ration it. Accounts above a line get a person who learns them. Everyone else gets documentation and a form. The hive serves all twelve holdouts because it is billions. You cannot hire billions.

So we built the small version, pointed it at our own website, and measured.

Nine weeks, 15,012 people

Rover is the agent that lives on rtrvr.ai, our own platform. It gives visitors live demos, onboards new customers, answers questions, and does tasks in the real product — a forward-deployed engineer for every account — and it records what it sees. Over nine weeks that came to 15,012 people, 1,583 of whom engaged, and 11,121 typed requests, counted per person rather than per session.

The question: how much of an expert's knowing can be rebuilt from behaviour alone? What follows is the whole method, including the parts that did not work.

Reading a request

Embedding raw transcripts fails. Most of a conversation is greetings, filler, and the agent's own replies — embed that and you get clusters of "hi, thanks, can you help" instead of what anyone actually wanted. Anthropic showed the fix with Clio in 2024: extract first, embed second. A small model reads each session and emits one line of intent. That line gets embedded.

We ran that over every request for roughly the price of lunch. It left 10,484 intent lines , 11.7% of them not in English.

Which clustering method actually wins

Most write-ups assert a choice. We ran the candidates over the identical corpus with identical metrics.

k-means with twelve clusters puts 39% of everything into one bucket. It does so because k-means cannot say this request is not like the others — every point must land somewhere. HDBSCAN's largest cluster holds 3.5%, because it is allowed to leave things out.

It left out a lot: 22.2% unassigned . We kept that number rather than tuning it away. Those are the genuinely one-off requests, and a system that pretends they belong somewhere will confidently mislabel them.

For a buyer, this one chart is the difference between a report that says "your top topic is Support" and one that names 210 specific things your customers tried to do.

The parameter nobody publishes

HDBSCAN has no k, which gets described as "no parameter to guess." That is not true — the guess moves to min_cluster_size.

The same corpus yields 515 topics or 31 topics depending on one integer. There is no correct value. We shipped 15 — matching the value Intercom's Fin team published — which gives 210 intents , and we are telling you the number so you can discount it.

The shape of the result matters more than the count:

112 of the 210 clusters hold fewer than 30 requests. That tail is the entire point. A dashboard ranked by volume shows you the top ten and hides the other two hundred — which is where the specific, fixable, expensive problems live. Twenty-four people asking to extract job listings is invisible in a monthly rollup and is also a feature request with two dozen signatures.

Is a single point an honest summary?

Production inference keeps one centroid per cluster and assigns new requests by similarity — milliseconds, not minutes. It also quietly assumes clusters are round; if they are stretched or lumpy, the centre sits in empty space and every assignment after it is wrong. So we measured the shape of all 194 measurable clusters.

Aspect ratio: median 1.50, and 97.4% fall between 1 and 2.5. Compact, not stretched.

Spectral entropy: median 0.862 , where 1.0 is a perfect sphere. Fin published 0.6–0.8 on support conversations and called their clusters "egg-shaped rather than round." Ours are rounder.

Separation 0.82 mean inter-centroid cosine distance, cohesion 0.29...