Alium.

A market where nobody trusts the reviews

Here is a slightly awkward fact about enterprise software: companies spend an enormous amount of money on it - roughly 5.5% of revenue, by common estimates - and they make many of those decisions using research the vendors themselves paid to appear in.

This is the puzzle Alium is built around. If you are a VP of marketing choosing a loyalty platform, or a CISO signing off on a security tool, the "objective" resources available to you have a funny structural problem: the analyst reports are expensive and abstract, and a good chunk of the online review marketplaces run on some flavor of pay-to-play. Everybody in the room knows this. The buyers know it, the vendors know it, and the analysts sort of know it too. And yet the purchase still has to happen, so the buyer does the thing humans always do when the official data is suspect - they text a friend at another company and ask what actually works.

Alium's entire thesis is that this back-channel text message is, in fact, the most valuable research in the market. So the company set out to build the infrastructure for it: a private network of technology decision-makers, a disciplined way to interview them, and an AI layer that reads hundreds of those conversations and turns them into something you can search, sort, and rank. The tagline is not subtle about the target - "Analysts don't know tech. VPs of marketing do. CISOs do. IT directors do."

Three verbs, one platform

Most of enterprise software buying comes down to three moves, and Alium organizes the product around them. The clever part is not any single feature - it is that all three run on the same underlying asset: honest, compensated conversations with people who use the tools every day.

On the other side of the marketplace sits the vendor product: anonymized, aggregated market insight that lets software companies see how buyers actually perceive them and how they stack up against competitors. It is the same data, pointed in the opposite direction - and it is where a good deal of Alium's revenue pipeline comes from.

The A-List, or: what happens when users pick the winners

Illustrative of the categories covered by the 2025 A-List. Full rankings at research.alium.io/a-list-2025.

Why "AI-powered" is the boring part

Every startup in 2025 says it is AI-powered, and most of the time that phrase is doing very little work. Alium is a case where it is more defensible - not because the model is special, but because the task genuinely needs it. Reading, tagging and synthesizing hundreds of long practitioner interviews by hand is not a job a human team can do at speed. The AI is what makes the raw material usable.

But the actual moat is upstream of the model. It is the private network of buyers who agree to talk, and the fact that Alium compensates them for honest time rather than blasting a free survey into the void. Good research turns out to be a relationship, not a form. Anyone can bolt a language model onto a pile of reviews; far fewer can convince a VP at Nike or a director at PepsiCo to sit for a candid, structured interview about the tools they regret buying. Get that flywheel spinning and the ratings improve on their own - which is roughly the same dynamic that made the founders' last company hard to copy.

A short history

Links & sources