ARES-C

The density and independence of sources that reference your organisation across the domains the machine treats as authoritative.

• Citation breadth across independent publications
• Source authority — peer-reviewed, archival, institutional
• The shape of the conversational graph around your name, across topics and sectors
• Cross-model consensus on which references the machine returns first

The machine assigns weight not to what you say about yourself but to what others have said about you in contexts you did not control. Organisations with thin Authority are frequently accurate in the machine's mouth — but never recommended.

Authority is the slowest dimension to build because it sits outside your perimeter. Repair begins with mapping the sources the machine already trusts in your category, identifying which of them carry none of your signal, and establishing a sustained contribution discipline that corrects the asymmetry over years — not quarters.

The sentiment, warmth and confidence with which AI describes you, read across systems and across time.

• Tonal register across systems when the machine is asked about you
• Confidence calibration — whether the machine hedges or asserts
• Competitive framing — returned as the reference, or as one of several
• The trajectory of sentiment across the last thirty-six months

A brand can be present in every relevant answer and still lose every relevant decision, because the machine describes it in the wrong tonal register. The difference between the first choice in the category and one option in the category is not semantic. It is commercial.

Faster than Authority, harder than it looks. It requires synchronising the tone across every surface the machine reads — press, site, partnerships, interviews, published work — so that the register the machine eventually adopts is the one the brand itself would choose, if given the choice.

The gap between the expertise you actually possess and the expertise the machine can see.

• The distance between practised expertise and published expertise
• Structured topical representation — whether your real specialisation is visible in the retrieval graph
• Depth of representation per named capability
• The quiet-expert problem — senior figures whose work is consequential but machine-invisible

Many of the most accomplished organisations and individuals are read by the machine as thinner than they are — because their work lives in rooms, projects, client engagements and peer relationships, none of which are machine-readable. In an AI-mediated world, the expertise that cannot be surfaced cannot be chosen.

The most rewarding dimension to repair, because the material already exists. The intervention is not to generate expertise but to catalogue the expertise that was never catalogued — publicly, independently, and at a depth the machine will weight.

The structural coherence of your public record — not its volume.

• Consistency of positioning across the full history of public expression
• Coherence across channels: site, press, product messaging, partnerships, interviews
• Drift across management transitions
• The ratio of signal to noise, measured against itself over time

In the language of the machine, inconsistency becomes invisibility. The brand that has said one true thing about itself for a decade outranks the brand that has shouted a dozen different things loudly.

The hardest dimension, because it is cumulative — the record is the record. The work is not to fabricate retroactive coherence but to establish a new sustained coherence going forward, clean enough and maintained for long enough that the machine eventually weights the new pattern more heavily than the old.

Whether the machine has placed you in the right category, with the right peers, against the right competitive set.

• Category assignment across models
• Peer-set composition — who you are being compared to
• Frame accuracy — read as premium, volume, niche, or misclassified entirely
• Misalignment between your intended frame and the machine's resolved frame

A luxury manufacturer read as a volume producer, a premium service firm read as a commodity provider — these are context errors, and they quietly rewrite every other reading the machine produces.

Often the fastest to diagnose and the most strategic to correct, because a small upstream shift in how you are framed can restore correct interpretation across hundreds of downstream readings. The repair begins with locating where the frame broke — usually a category assumption made years ago, on a surface nobody has been monitoring since.

Every cycle reads nine pinned AI models, drawn from the Western, Eastern and Middle East & Africa spheres, so no single model’s opinion — and no single region’s training — carries the reading. The model actually served is recorded alongside the model requested.

ARES-C is anchored to observable AI behaviour — is the brand named, recommended, described correctly — not to taste. Most of the score is read by extraction: counted, reproducible, hard to argue with.

No person reads, adjusts or approves a score. Where an answer has to be classified rather than counted, a pinned AI evaluator applies a standardised, versioned rubric every cycle. Every cycle runs on pinned model versions, to help us tell a brand's movement from a change in a model. We check model versions every cycle and log any change we find, with the date we found it. Before each cycle runs, a control check confirms the system is working as expected. Human judgement enters only in the Signal Audit, after the measurement — never inside it.

Scores are published to one decimal place, the same on every Pitot page.