What AI decides about your brand before the board hears about it.

Uncover the sources

We lay open which publications, dealers, forums and platforms carry the AI answers in your category. That is the list that makes press work targeted at all: not reach, but the domains AI actually cites.

Measure per country what AI thinks

The same corpus in the local language, because AI answers from different sources per language. We see where the brand counts as a fashion label in Germany and does not appear in France at all, and where a competitor owns the recommendation.

Prepare the product launch

Before launch we check which terms, model names and claims AI already knows and which it confuses with the predecessor. Nomenclature and warranty statements are approved before measurement, otherwise the study itself produces the false statement.

Brand and product perception

What does AI think about the brand, about individual products, about quality, price and service? We analyse tone, attributions and confusions per cluster and set them against what the enterprise says about itself.

Agentic purchase test

We let shopping agents buy your products and log whom they pick: own store, dealer, marketplace or competitor. From that follows the agentic D2C decision, which Bain splits into three paths: open up to third-party agents, build your own agent, fortify your own site.

Analyse and decide

Measurement and research turn into the document: which content is missing on the website, which media partners close the gap, how social media and dealer communication are aligned. With responsibility band, sequence and a measurement plan for the repeat.

The sources almost never belong to the brand.

For a US workwear brand, 0.9 percent of all evidence cited by AI came from its own domain. The rest came from dealers, trade press and forums. Whoever wants to change the answer has to work on those domains, not only on their own website.

Own measurement, 500 buying questions across five AI systems, Germany 2026.

AI holds its own image of the brand.

The same brand was named in 72.5 percent of answers to style questions and in zero percent for high-visibility workwear. AI placed it as a fashion label, not a safety brand. The enterprise had communicated the opposite. Only the measurement made the gap visible.

Cluster comparison per intent instead of one overall grade.

The pre-selection breaks before sales.

In the consideration phase AI named competitors in 60.5 percent of answers and the brand in 12.9 percent. For questions naming the brand it stood at 99.2 percent. The brand does not lose the customer who knows it, but the customer who is still searching.

Branded and unbranded measured separately.

Buyers check the brand, they do not search the category.

For an automotive OEM, branded search demand stood at 53,650 queries a month, unbranded segment demand at 30,380. The buyer knows the model and checks it. So the corpus has to measure both: the recommendation for open questions and the answer for brand questions.

Own research from search volume, forums and dealer enquiries.

The strongest need was on no slide.

Dealer search was the same OEM's largest measurable need, more than range, price or software. The marketing material did not mention the dealer network. Research from search data and 1,500 forum comments turned the priority around.

Topic density in forums and search volume per cluster.

At launch, nomenclature decides.

A new model carried a warranty that did not apply to the established sister model. Without separation in the corpus the measurement would have produced the false statement itself. That is why terms, claims and warranties are approved before the first measurement.

Launch measurement with a fixed benchmark sample, before and after.