ARA measures how visible, legible, and recommendable your brand is to the AI systems your customers are already using — and tells you exactly what to do about it.
When a customer asks an AI assistant what to buy, which brand to trust, or which product to choose — they get an answer. That answer is not random. It reflects decades of structured and unstructured data: reviews, editorial content, brand language, community advocacy, and technical infrastructure.
Brands that have invested in machine-legibility win. Brands that haven't are losing recommendations they don't even know they're missing.
"For five thousand years, every brand decision ever made had one thing in common: a human was at the end of it."
— Brand After Humans · jaredkiwi.substack.com
The first wave was discovery. AI systems learning to answer "what should I buy?" That wave is already here, and most brands are unprepared for it.
The second wave is purchase. AI agents with access to payment rails, executing transactions autonomously — no human at the checkout, no last-minute brand preference, no impulse reconsideration. The agent picks. The agent buys.
In that world, the brands that win are the ones machines already know, trust, and consistently recommend. The window to build that machine presence is now — before the decision is no longer a recommendation at all.
ARA produces the most rigorous independent assessment of brand algorithm readiness available. Each study covers a defined competitive set, scored across five proprietary dimensions — from structural data infrastructure to emotional residue in machine training data.
Beyond the score, ARA delivers a prioritised roadmap: specific, actionable recommendations for improving machine visibility, semantic clarity, and recommendation win rate. The question we answer is simple — does your brand know itself clearly enough that a machine can understand it?
"Most brands are optimised for human attention. Very few are optimised for machine memory."
ARA works with brand owners, CMOs, and their agency partners. Studies are commissioned on a category basis — a defined competitive set is assessed together, providing the benchmarked context that a single-brand audit cannot.
Authenticity is a technical advantage. Real brand love produces distinct linguistic patterns machines can identify — different vocabulary, specificity, and frequency than manufactured marketing. We help brands build and demonstrate that distinction.
All results are confidential and delivered directly to the commissioning client.
Tell us about your brand and category. We'll be in touch to scope the study and outline what the audit covers.
Studies typically cover 6–10 brands within a defined category. Single-brand audits are available on request.