An international group with a portfolio of six small appliance brands and ~3,500 SKUs across three marketplaces replaced subjective visual assessments ("10 people — 10 opinions") with a data-driven methodology inside an isolated infrastructure perimeter.
~3,500 SKUs in the "premium-mass" niche are distributed via Ozon, Wildberries, and Yandex Market. A new Head of Commercial Content and e-commerce marketing and SEO/GEO teams initiated a review of the listing management approach.
The "audit tool" request grew into a fully-fledged agentic system during scoping — a continuously running AI loop inside the client's perimeter that aggregates data, generates insights, and raises alerts without human involvement in routine tasks.
Content management was built on expert opinions, and those opinions systematically diverged: "if you seat 10 people at a listing, you'll get 10 different opinions." Visual revisions went through 4–5 functions; approving a single carousel took 3–4 weeks, with no arguments to present to management. There was no alignment between SEO semantics, U&A research, and design.
Audits were done in isolation — 3–5 SKUs in Excel; competitors were analyzed quarterly, and reaction to their changes lagged by 4+ weeks. Cross-marketplace content consistency checks were done by eye. The top of search results was treated as the benchmark, ignoring the price index, stock levels, and performance budgets.
9 specialized AI agents replace subjective listing evaluation with a data-driven methodology: audit, competitive benchmarking, SEO, and compliance — all within an isolated environment.
The reasoning layer is split into 9 specialized agents (Creative, Copy, SEO/GEO, QA, Analytics, Competitive, Publishing, and others) with autonomy policies ranging from "read-only" to "with publication rights." The orchestrator assembles verdicts into a report: 6 criteria × 19 SKUs × 3 marketplaces = 342 verdicts, each with a 3–5 sentence rationale and source reference.
The knowledge base holds brand guides, platform rules, NDA-protected U&A, and semantics across 6 categories. Each agent is required to cite a source; rule versions are preserved to reproduce any past decision. This resolved the core compliance concern — confidential research is used without leaving the perimeter.
A Competitive Intelligence Agent with a virtual browser collects ~130 competitor listings in the same price segment; multi-modal CV converts each into a vector and clusters them in 1024-dimensional space, identifying the dominant category pattern. This removes the "top of search = benchmark" trap.
Assessment is made not relative to the entire market, but relative to the "brand × category × segment" intersection: positioning, buyer profile from U&A, brand guides. Simulated eye-tracking and attention maps provide a predictive before/after CTR estimate — an A/B test before the A/B test.
The team received infrastructure that removes the blocking conflict between functions: instead of exchanging opinions, there is a shared data-driven foundation that can be shown to management and approved without debates about taste.
This unblocked the transition from one-off audits to daily monitoring of the full assortment and laid the groundwork for retail media, supply chain analytics, and review and VoC analytics — adding a new agent or connector takes hours, not weeks.
Now we know exactly what to change on each listing and why — with a rationale, a reference to a source in our own knowledge base, and a predictive impact estimate. Before, 10 people gave 10 opinions; now we have one system with transparent logic that can be shown to management.
The metrics and results presented reflect outcomes of a specific project and depend on its initial conditions. They are provided for informational purposes only, do not constitute a public offer, and do not guarantee similar results in other projects. Supporting materials are available on request.
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