List of Yandex Neuro Customers
Moscow, 119021,
Russia
Since 2010, our global team of researchers has been studying Yandex Neuro customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Yandex Neuro for Generative AI Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Yandex Neuro for Generative AI Platforms include: Magnit, a Russia based Retail organisation with 386000 employees and revenues of $29.30 billion, Lamoda, a Russia based Retail organisation with 8000 employees and revenues of $1.11 billion, Petrovich, a Russia based Distribution organisation with 2500 employees and revenues of $650.0 million and many others.
Contact us if you need a completed and verified list of companies using Yandex Neuro, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Yandex Neuro customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Lamoda | Retail | 8000 | $1.1B | Russia | Yandex Cloud | Yandex Neuro | Generative AI Platforms | 2025 | n/a |
In 2025, Lamoda deployed Yandex Neuro on Yandex Cloud to accelerate analytics across its Russian e-commerce operations. The implementation emphasizes Generative AI Platforms capabilities to analyze e-commerce metrics, seasonality, and merchandising performance for product and marketing teams.
Lamoda leverages Yandex Cloud's Neuroanalyst module, inferred to be used within Yandex DataLens and Yandex AI Studio, to operationalize model-driven hypothesis testing and role-specific reporting. Functional configuration focuses on automated data ingestion from Lamoda's commerce telemetry, prebuilt forecasting and segmentation routines, and an experimentation workspace to shorten decision cycles for merchandising and promotion planning.
Integrations center on Lamoda's internal e-commerce metric streams and analytics data layers to provide contextualized outputs for product and marketing workflows. Governance uses role-based access and staged rollouts to enable analysts and marketers to consume generative insights, with the explicit goal of speeding hypothesis testing and decision-making and delivering faster, role-specific analytics.
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Magnit | Retail | 386000 | $29.3B | Russia | Yandex Cloud | Yandex Neuro | Generative AI Platforms | 2025 | n/a |
In 2025, Magnit deployed Yandex Neuro to accelerate business intelligence and analytics across its retail operations in Russia. The Yandex Neuro implementation is delivered via Yandex DataLens and Yandex AI Studio on Yandex Cloud, positioning the Generative AI Platforms capability directly within the company reporting and dashboarding layer.
The implementation leverages Neuroanalyst capabilities to automate insight synthesis, support natural language querying of datasets, and surface prioritized anomalies and trend summaries for inventory, promotions and store performance. Yandex Neuro is used to augment existing dashboards and to generate narrative insights that reduce repetitive analyst tasks while speeding time to actionable findings.
Integrations are structured around Yandex DataLens and Yandex AI Studio ingestion pipelines, consuming point of sale, inventory and promotional telemetry to produce consolidated analytics views for merchandising, store operations and regional management. Deployment architecture uses Yandex Cloud native services to host the generative inference and data visualization stack, enabling centralized model access alongside distributed retail data feeds.
Governance and operational workflows were adjusted to embed AI-generated narratives into analyst review cycles, shifting analyst effort toward validation, escalation and orchestration of recommended actions. The stated objectives are faster insight generation and reduced load on internal analytics teams, with Yandex Neuro serving as a front-line synthesis layer within Magnit’s BI footprint under the Generative AI Platforms category.
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Petrovich | Distribution | 2500 | $650M | Russia | Yandex Cloud | Yandex Neuro | Generative AI Platforms | 2025 | n/a |
In 2025, Petrovich implemented Yandex Neuro. Petrovich uses Yandex Cloud's Neuroanalyst for retail and logistics analytics to identify demand patterns and support operational decisions across its Russian footprint.
The implementation leverages capabilities aligned with Generative AI Platforms to produce rapid, explainable insight generation for merchandising and inventory planning. Configuration emphasizes demand pattern detection and assortment signal generation, with models tuned to retail seasonality and distribution lead times to inform supply and assortment planning workflows.
Deployment is cloud-native on Yandex Cloud, with Yandex Neuro running as an analytics layer that feeds insight outputs into operational decision processes across Petrovich’s stores and distribution centers in Russia. The implementation scope covers retail and logistics functions, with analytics delivered to planning teams and category managers to support replenishment and assortment choices.
Governance is organized around analytics and supply chain stakeholders, focusing on iterative model tuning and rapid insight delivery rather than large batch reporting. The stated operational objective is to accelerate insight-to-decision cycles to improve supply and assortment planning outcomes.
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