Enterprise Commerce Intelligence · Wyoming, USA
Clovty applies AI-powered analysis to marketplace, search, and consumer-signal data — building research for the category bets, pricing changes, assortment rationalizations, and market-entry assessments that institutional commerce teams rely on. Every brief is sourced, written, and built to hold up in front of a board or investment committee.
Client profile
Clovty advises leaders responsible for category, assortment, pricing, and expansion decisions at scale. Engagements are typically structured for the following types of organizations:
Solutions
Engagements are fixed in scope, defined in writing, and delivered with source attribution. Clovty operates on a project- and retainer-based engagement model focused on commerce intelligence and decision-grade research.
Quantitative mapping of competitive density, pricing dynamics, and demand trajectory within a defined category. Used by category managers entering or rationalizing a market, and by investors validating market-size or growth claims. Each project ships as a written brief with confidence-rated findings, plus the underlying dataset for client analysts to extend.
Bespoke analytical work for questions that don't fit a standard category brief — assessing the durability of a private-label launch, modeling cross-elasticity between SKUs, classifying review-text signal at scale, or testing a pricing thesis against historical demand. Methodology is documented in every deliverable.
Continuous monitoring of marketplace signals and competitive shifts, with periodic alerts on category inflection points and demand surges.
Quantitative diligence on digital-commerce assets: revenue quality, customer concentration, organic-versus-paid mix, and category positioning. Built for M&A teams, growth investors, and corporate development. Findings are confidence-rated and tied to source data, with sensitivities stated explicitly.
Methodology
Machine-learning analysis ingests, normalizes, and clusters commerce data at scale. A senior analyst then synthesizes the model output into actionable conclusions. The separation between quantitative breadth and analytical judgment is what makes Clovty deliverables suitable for boardroom and investment-committee review.
Marketplace listings, search-interest curves, content-platform signal, third-party datasets, and macro-level commerce data are pulled on a defined cadence. Coverage is scoped to the question at hand — a category-mapping project may draw on two dozen marketplaces; a single-SKU diligence may focus on three. Source documentation is preserved at every stage so each later claim can be traced back.
Scoring models assess trend persistence, competitive density, margin viability, and demand quality. NLP processes review text for sentiment, pain-point clustering, and feature-level signal. Anomaly detection flags unusual velocity, price-move patterns, and competitive entries. Outputs are intermediate — they describe what the data shows, not yet what the client should do.
A senior analyst reviews model output, applies domain judgment, and authors the conclusion. Each claim is sourced. Confidence levels are stated explicitly. Methodological limitations are disclosed.
The client receives a written brief, the working dataset, a source register, and a debrief session with the engagement lead. Thirty days of follow-up access are included in every project.
Data & sourcing
Clovty's methodology relies on publicly available marketplace data, third-party datasets accessed in accordance with their licensing terms, and authorized search signals.
Next step
Submit a brief description of the question your team is examining. Clovty responds within two business days with an indicative scope and proposed timeline.
Schedule an introductory call