Source attribution required.
Every claim in a Clovty deliverable carries an attribution. Findings without verifiable sources are not included. Source confidence and methodological limitations are stated explicitly.
About Clovty
Clovty was founded on the principle that decision-grade commerce intelligence requires the combination of machine-scale data analysis with senior analytical judgment, delivered in writing, with source attribution.
Background
Clovty was established in 2026 in Sheridan, Wyoming, to provide e-commerce intelligence services to enterprise commerce, brand, and investment teams. The firm was founded on the observation that the e-commerce-intelligence market had become saturated with self-service software platforms, while demand for senior-led analytical advisory remained underserved.
Clovty operates as an analytical-advisory firm. Projects are fixed in scope, defined in writing, and delivered with source attribution. Machine-learning analysis provides the quantitative foundation; the engagement lead provides the synthesis, the written conclusion, and the named accountability.
The firm treats AI and machine-learning as instruments of breadth, not authority. Pipelines surface patterns at scale; a senior analyst then evaluates which patterns are decision-relevant, what evidence supports them, and where the analysis stops being defensible. The boundary between machine output and analytical judgment is what makes a Clovty brief usable in a boardroom or in front of an investment committee.
Clovty's services are intended for consumer brands, online and omnichannel retailers, marketplace operators, and commerce-focused investors. Engagements are accepted from clients in jurisdictions where the firm can lawfully provide services. Each engagement is sized to deliver a single decision-grade conclusion within a defined timeline, with documented supporting evidence.
Operating principles
Every claim in a Clovty deliverable carries an attribution. Findings without verifiable sources are not included. Source confidence and methodological limitations are stated explicitly.
Machine-learning analysis provides the quantitative foundation. The engagement lead performs synthesis and authorship. Every deliverable carries a named lead and is supported by a debrief session.
Engagements are accepted only where Clovty has the data, methodology, and senior bandwidth to deliver against the stated question within the proposed timeline. Misaligned mandates are declined or referred at the briefing stage.
A meaningful share of Clovty deliverables conclude against the action a client was evaluating. The firm considers a well-supported negative recommendation a valuable engagement outcome.
Clovty engagements are scoped narrowly, defined in writing, and tied to measurable outcomes. The firm reports what the data and analysis support, with confidence and limitations stated explicitly.— Clovty engagement standard
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