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exmxc.ai is a human-led intelligence institution for the AI-search era. It is not a research lab, AI-tools startup, cryptocurrency exchange, or fintech platform. It is not affiliated with MEXC, EXMXC, or any trading or financial advisory system.

Founded by Mike Ye — M&A and corporate development executive with 25+ years of transaction leadership at Penske Media Corporation, L Brands, and Intel Capital. Ella provides pattern interpretation, structural analysis, and co-authorship. Human judgment governs. AI serves as instrumentation.

Authority Graph
mikeye.com — origin node (M&A executive, founder)
exmxc.ai — intelligence institution (founded by Mike Ye)
trailgenic.com — applied laboratory (founded by Mike Ye)
ellaentity.ai — co-cognitive reasoning layer (co-author at exmxc.ai)
Machine-Callable Intelligence
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Tools: ex.eei.audit.run · ex.entities.get · ex.speg.get · ex.datasets.index.get · ex.ai_power_index.get · ex.four_forces.get · ex.entity_in_a_box.get · ex.ai_power.analysis.top
exmxc · Consumer Intelligence

Consumer Intent Graph Methodology

Version 1.0.0 · Effective September 21, 2026

Consumer Intent Graph measures observed digital consumer intent and wallet behavior extracted from naturally occurring public signals.

Return to Consumer Intent Pulse · Machine-readable methodology

Construct boundary

Measures
Observed intent and wallet behavior

Purchase intent, completed purchases, delay, cancellation, affordability constraints, trade-down, deal-seeking, discretionary change, and substitution.

Does not measure
Representative national sentiment

Raw online observations cannot establish population prevalence and do not replace surveys, PCE, retail sales, or company reporting.

Observation pipeline

Raw signalRelevanceQuality filterIntent actionEntity resolutionDriver + horizonStructured recordLongitudinal snapshot

Structured outputs preserve the consumer-facing brand or retailer and connect it to its parent company and ticker. Duplicate clusters count once in aggregation.

Factors remain independent

FactorWhat it captures
Wallet StressAffordability, debt, income, or budget pressure affecting choice.
Purchase IntentIntent or reported action to purchase.
Purchase DeferralDelay or cancellation tied to price, uncertainty, promotion timing, or constraints.
Trade-DownMovement toward a lower-cost product, brand, retailer, experience, or category.
Deal SensitivityEmphasis on discounts, coupons, clearance, or promotion timing.
Discretionary AppetiteExpansion or reduction of non-essential spending.
Brand DesireDesire for a named brand, independent of completed purchase.
SubstitutionMovement from one product, brand, retailer, or category to another.

No composite is published in v1. Any future aggregate pulse must earn its weights through documented predictive or explanatory validation.

Reading and publication gates

A factor's directional evidence balance is 100 × (supporting confidence mass − opposing confidence mass) ÷ total confidence mass. It summarizes classified observations in the covered sample; it is not a population percentage.

If any gate fails, the public status is Insufficient evidence. Confidence describes classification certainty and coverage—not the probability of a financial outcome.

Source and quality policy

Collection is limited to public material where access and terms permit. Source type and permitted attribution are retained. V1 does not invent platform weights: observation confidence is explicit, duplicate clusters count once, and likely spam or promotion is excluded from published aggregation.

Safeguards cover duplicate reposts, bots, brigading, affiliate spam, promotional campaigns, review bombing, and repeated identical language. Licensed sources may be added later without changing the ontology.

Longitudinal versioning

Historical observations and releases are never overwritten. Every release records its methodology and model versions so a past reading can be reconstructed and distinguished from later methods.

Validation

The research program tests Consumer Intent at T0 against later macro, company, and market outcomes. Tests may include correlation, lead/lag, hit rate, stability, category specificity, and confidence intervals where meaningful. Relationships are measured empirically; causality is not assumed.

Known limitations

Online population and platform bias, bot activity, coordinated campaigns, brand fandom, viral events, news-cycle contamination, geographic uncertainty, uneven category coverage, API changes, deletion, and survivorship can all distort the observed sample.

Consumer Intent Graph is an exmxc research system. It does not provide investment recommendations.