Open any ten AI product sites and the claims are interchangeable. Accurate. Intelligent. Fast. Enterprise-ready.
Words that every competitor also uses carry no information. In a saturated category, the only thing that differentiates is evidence, and evidence has to be manufactured deliberately.
Publish a number, and the method behind it
The single most effective thing an AI company can do is state a measurable claim and show how it was measured.
Not “highly accurate”. A specific figure, on a named task, against a dataset, with the methodology published so someone could reproduce it.
This is uncomfortable because it is falsifiable, which is exactly why it works. A claim that could be checked and has not been contradicted is worth far more than an adjective that cannot be checked at all.
It also requires the evaluation infrastructure to exist. Companies that build an eval harness for engineering reasons end up with their best marketing asset as a side effect.
Show where it fails
Almost nobody does this, which is why it is so persuasive.
Buyers in this category have been disappointed repeatedly. They have seen demos that worked beautifully and products that did not. They arrive sceptical and looking for the catch.
A product page that says plainly where the system underperforms — which document types, which edge cases, what it should not be used for — does something no amount of positive claiming achieves. It signals that the rest of what you said is probably also true.
It also qualifies buyers honestly, which shortens sales cycles and reduces churn from people who bought the wrong thing.
Demos have to accept the visitor’s input
A curated demo is transparent to anyone who has seen a few.
The demo that converts lets someone try their own input — their document, their query, their data — and see what happens. It is harder to build, occasionally embarrassing, and dramatically more convincing.
If the product genuinely cannot be demonstrated on arbitrary input, say so and explain why, rather than showing a reel and hoping nobody notices they cannot touch it.
Benchmarks, including unfavourable ones
Publishing only benchmarks you win is a recognisable pattern and technical buyers discount it automatically.
Publishing a full comparison, including where a competitor beats you, is read completely differently. It suggests the numbers were not selected, which makes the favourable ones believable.
State the method. A benchmark someone else cannot reproduce is marketing. One they can is evidence.
Sell to the whole committee
An AI purchase in any serious organisation involves more than the person who wanted it.
Engineering asks about architecture, latency, failure modes and integration. Security asks where data goes, what is logged and what happens with a provider. Finance asks what happens to cost as usage grows, which is a genuine anxiety given how AI pricing behaves.
Material addressing only the enthusiast addresses a third of the decision.
The cost question deserves a real answer
Buyers have been surprised by AI bills and they have learned to ask.
A vague answer reads as evasion. A clear explanation of how pricing scales, what drives it, and what controls exist — routing by difficulty, caching, caps on agent loops — is reassuring in a way that a pricing table alone is not.
Build the evidence and the marketing together
This is the part that has to be structural rather than a campaign decision.
If the evaluation harness exists, the marketing has numbers. If it does not, the marketing has adjectives, and adjectives are what everyone else already has.
We build AI products and market them, which means the claims get written against measurements that exist rather than against an aspiration — and in this category, that is the whole differentiator.
Common questions
How do you market an AI product?
By publishing evidence rather than adjectives. Every competitor claims accuracy, speed and intelligence, which means those words now carry no information. What differentiates is a measurable claim against a dataset you can show - and that requires the evaluation infrastructure to exist alongside the marketing rather than after it.
What makes AI marketing credible?
Specific numbers against a named benchmark or your own published golden set, honest limitations, and a demo that works on inputs the visitor chooses rather than ones you selected. Buyers in this category have been disappointed repeatedly and have become good at spotting a curated demo. Showing where the product fails is unusually persuasive precisely because almost nobody does it.
Should AI companies publish benchmarks?
Yes, including unfavourable ones, and with the methodology stated so results can be reproduced. A benchmark you defined, ran and cannot explain is treated as marketing. One with a published method and an honest account of where you underperform is treated as evidence, and it is what technical buyers actually look for.
How do you sell AI into cautious industries?
By building the evaluation story and the marketing together, so every claim in the material traces back to a verifiable result. Institutional buyers and their legal teams spot overclaiming quickly, and a single unsupportable number costs more trust than three good ones earn. The credible path is measurements against a dataset you can show them.
Does Corum8 market AI products?
Yes, and we build them - agents, retrieval systems and evaluation harnesses. That means the marketing claims get written against measurements that actually exist, which is the only durable differentiator in a category where everyone says the same words.
