The Salienci Score - Measurement Methodology.
Salienci measures Recommendation Authority - whether AI answer engines independently select, cite, and recommend a business when buyers ask.
This document publishes what we measure, how we measure it, how often, and where the known limits are. The composite weights and probe construction are proprietary; everything else is stated here in the open.
What We Measure - and Why Visibility Is Not Enough
Being visible to AI is not the same as being recommended by AI. An answer engine must complete three movements before it recommends a business: it must reach and identify the entity, extract a citable answer from its content, and find the wider ecosystem corroborating that answer. The Salienci Score is built on exactly that causal chain - three driver pillars, each auditing one movement.
| Pillar | Question it answers | What is audited |
|---|---|---|
| Agent Readiness | Can a machine reach, render, and verify you? | Crawler access, rendering, structured identity, entity verification, business-profile integrity |
| Answer Quality (AEO) | Can a machine extract a citable answer? | Content structure, answer targeting, citation readiness, factual anchoring, content quality |
| Entity Visibility (EVS) | Does the ecosystem vouch - and does AI cite you? | Trust and review signals, authority signals, community presence, and the measured citation outcome |
A fourth band - Foundation (conversion, usability, measurement instrumentation, accessibility and governance) - is audited and repaired but reported as a separate Digital Presence Score. It is never blended into the authority result, because fixing a contact form does not make an AI more likely to recommend a brand. Keeping the two apart keeps the headline score honest.
How the Outcome Is Measured
The pillars audit inputs; the outcome - who AI actually recommends - is measured directly on a fixed monthly cadence using a structured probe program. Each probe run asks answer engines the questions real buyers ask in the category, then records whether the brand is named, how it is described, and which source is cited.
Buyer-intent prompts
ProbeA bank of real category-buyer questions spanning five intent classes (discovery, capability, trust, comparison, and natural phrasing), localized to the client's service area and, where relevant, language.
Four leading AI assistants
ProbeEach probe cycle runs across ChatGPT, Gemini, Perplexity, and Claude, with video and community ecosystems (YouTube and Reddit) measured through their own channels - because answer engines demonstrably draw on both.
Seven citation-outcome dimensions
ProbeEach cycle records how often the business is mentioned, where in the answer it appears, whether the stated facts are accurate, how many distinct platforms cite it, how fast citation frequency is changing, and its visibility in the video and community sources engines draw on.
Fixed re-score gates
ProbeEvery engagement is re-scored on the same probe bank at set intervals, so movement is measured against an identical instrument - not a moving target.
The Content Quality Gate
Every piece of content produced through Salienci passes a 475-rule validation framework before publication, applied in three tiers: deterministic structural checks, linguistic analysis, and AI-judgment evaluation for dimensions that require reasoning. Content that fails is returned for correction rather than published and hoped for.
Published content is then re-measured through the same probe program that scored the baseline, closing the loop between diagnosis, production, and outcome. The gate is applied identically to every client, so a passing score means the same thing across engagements.
Attribution Confidence - Never Guessed
When Salienci reports that AI visibility produced a lead, a call, or a booking, the claim carries one of five confidence tiers. We publish the taxonomy because an attribution claim without a confidence label is marketing, not measurement, and the tier tells you exactly how much weight the claim can carry.
| Tier | Meaning |
|---|---|
| CONFIRMED | Verifiable proof - an advertising click identifier, a matched customer record, or a verbatim call statement. |
| HIGH | Strong signal - an AI assistant or search engine detected as the referring source. |
| MODERATE | Declared by link tagging or aggregate estimate; not independently verified. |
| CONTRIBUTING | The channel helped but did not solely cause the lead. Partial credit only. |
| UNKNOWN | Evidence insufficient. Reported honestly - never guessed. |
Data Integrity Commitments
Salienci commits to a fixed set of data integrity rules that govern how every score is produced: measured values are never substituted with estimates, sample sizes are always disclosed, and any figure that is illustrative rather than observed is labelled as such wherever it appears.
Provenance on every number
Every emitted score is labeled LIVE, CACHED, ESTIMATE, or UNAVAILABLE. Missing data is reported as UNAVAILABLE - it is never invented.
Ranges, not point promises
Score targets are always expressed as ranges. A single-digit promise about a probabilistic system is a sales device, not a measurement.
Same instrument, every cycle
Baselines and re-scores run on the identical probe bank, so improvement is attributable to the work - not to a changed yardstick.
The rules governing how this document is reviewed, how figures are labelled, and how errors are corrected are published in the Editorial and Corrections Policy.
Known Limitations
A methodology that hides its limits should not be trusted with your budget. The Salienci Score is a sampled measurement of non-deterministic systems, so it carries real constraints on precision, repeatability, and coverage. The limits below apply to every engagement and are stated in every report.
- AI answer engines are non-deterministic. The probe program is a structured, repeated sample of real buyer demand - a rigorous sample, not a census of every possible question.
- Citation outcomes converge over roughly 60–90 days after access and content fixes land. Anyone promising instant citation movement is describing a system that does not exist.
- Benchmark comparisons are drawn from our measurement base, which is growing through our beta program; benchmark figures are labeled as such until the base supports broader claims.
- Third-party AI platforms change behavior without notice. Cadenced re-measurement is the control for this; immunity to it is impossible.
What We Do Not Publish - and Why
The composite weighting, the probe construction method, and the scoring mathematics are the audited core of the framework and are not published. They are available under NDA to clients and independent auditors, so the score stays verifiable without making the framework trivially reproducible.
The composite weighting, the probe construction method, and the scoring mathematics are the audited core of the framework and are not published. They are available under NDA to clients and to independent auditors. The distinction we hold to: the framework is the intellectual property; the score - and everything in this document - is the artifact you are entitled to understand before you rely on it.
Written methodology questions are answered publicly at salienci.ai. If we cannot defend an element of this method in writing, we will change the method - not the answer.
What the Salienci Score Does Not Measure
The Salienci Score measures whether AI answer engines can reach, understand, and independently recommend a brand. It does not measure revenue, brand preference, advertising performance, classic keyword rankings, or the commercial quality of the offer behind the brand.
- It is not a revenue forecast. A higher score means a stronger measured recommendation position, not a guaranteed commercial outcome.
- It is not a keyword ranking report. Classical search position is measured separately where a client asks for it and is never blended into the authority result.
- It does not evaluate pricing, product quality, or sales execution, all of which affect whether a recommendation converts.
- It does not measure private or logged-in AI experiences that cannot be observed through a repeatable public probe.
The measured inputs behind the score are explained layer by layer in the AEO+ framework and prioritized through the K-Matrix.
Data Quality, Missing Data, and Valid Comparisons
Every reported figure carries a data-quality state so a reader can tell measured values from partial or absent ones. Where evidence is insufficient, the report says so rather than filling the gap with an estimate, and comparisons are only published when the two sides were measured the same way.
Measured
The value was observed in a completed probe run or an automated technical check within the current reporting period.
Partial
Some probes or checks completed and others did not. The value is reported with its coverage stated and is not treated as a full-period result.
Insufficient data
Too few observations exist to report a stable value. The field is reported as insufficient rather than estimated, extrapolated, or carried forward.
Unavailable
The engine, surface, or source could not be reached during the period. The gap is disclosed in the report rather than shown as a zero.
When two scores can be compared
Two Salienci Scores are comparable only when they were produced under the same methodology version, the same category query set, the same engine set, and comparable probe coverage. Scores from different categories, different methodology versions, or different coverage levels are not equivalent and are not presented as a like-for-like comparison. Confidence labels travel with every comparison so a small movement is never read as a decisive result.
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