ENTERPRISE AI CASE STUDIES · VERIFIED ROI

Business cases

How companies use AI to grow.

52Verified Case Studies
7Industry Sectors
204+Primary Sources
AlpinaGPT: Enterprise Multi-Model AI Platform and RAG Built on a Verified Business Library
AD
Alpina Digital (AlpinaGPT)EdTech & B2B SaaS
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AlpinaGPT: Enterprise Multi-Model AI Platform and RAG Built on a Verified Business Library

Deploying AlpinaGPT across enterprise teams cut the time required to draft analytical briefs, training curricula, commercial proposals, and meeting minutes by 45–60%, while migrating staff away from…

45–60%time saved drafting briefs, proposals & training programs
3 000+licensed Alpina business books & courses in verified RAG
72%+active license utilization when paired with workshops
152-ФЗfull compliance with Russian data residency & security
Multi-LLM GatewayPostgreSQL + pgvectorHybrid RAG + Reranker
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Sberbank: AI resolves 65% of customer inquiries in the contact center
С
СбербанкBanking
✓ 6

Sberbank: AI resolves 65% of customer inquiries in the contact center

As of Q1 2026, AI resolves over 65% of customer inquiries: 66% in voice channels and 71% in chats. The market comparison sets the frame: per Frank RG's 2026 estimate, the industry average is 23% automation…

65%of inquiries resolved by AI (Q1 2026)
+7%operator productivity with GigaChat
+2 п.п.CSI growth after assistant rollout
300 млн ₽saved in 2023 (corporate call routing)
GigaChatБаза знаний SberHelpПредиктивная маршрутизац…
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Avito: Proprietary A-Vibe LLM and VLMs Automate Moderation and Sales Across 200M+ Listings
АA
Авито (Avito)E-commerce & Classifieds
✓ 3

Avito: Proprietary A-Vibe LLM and VLMs Automate Moderation and Sales Across 200M+ Listings

Over 98% of all marketplace listings are now moderated automatically without human intervention, while contextual chat analysis significantly reduced multi-step phishing incidents.

98%+of listings moderated automatically by AI without humans
200M+active listings powered by ML search and VLM indexing
−45%reduction in time to publish a listing from photos
21 млрд ₽projected economic impact from the GenAI platform rollout
A-Vibe LLM (8B)Custom RU TokenizerVision-Language Models (…
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Yandex Go & Lavka: Graph Neural Networks and Demand ML Orchestrate Millions of Rides and Dark Stores
ЯG
Яндекс Go и Яндекс ЛавкаMobility & Q-Commerce
✓ 3

Yandex Go & Lavka: Graph Neural Networks and Demand ML Orchestrate Millions of Rides and Dark Stores

Global RL-assisted dispatch and chained-trip matching reduced driver deadhead mileage and average passenger wait times by 15–22%, increasing driver hourly utilization without raising rider fares.

<2%perishable waste rate in Yandex Lavka via ML replenishment
−18%reduction in deadhead mileage and pickup wait time in Yandex Go
+25%increase in delivered orders per courier hour via batching
15 минconsistent hyperlocal grocery delivery SLA across 500+ dark stores
CatBoostGraph Neural Networks (G…Reinforcement Learning
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T-Bank: AI protection against phone scammers saved customers 1.2 billion rubles
ТБ
Т-БанкFintech
✓ 8

T-Bank: AI protection against phone scammers saved customers 1.2 billion rubles

The financial trajectory of 'Protect or Refund' has been published in stages: in its first six months it saved customers 170 million rubles; by May 2025 the cumulative amount reached 1.2 billion rubles,…

1,2 млрд ₽saved for customers cumulatively (by May 2025)
62 млнscam calls detected monthly by Neuroshield
44 000 чof scammers' time burned by Fraud Roulette in testing
12,3 млн ₽paid out in guarantee compensations
«Нейрощит» — детекция мо…Сервис «Защитим или верн…«Фрод-рулетка» — перехва…
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X5 Group: AI Dynamic Pricing, Demand Forecasting, and Computer Vision Across 25,000+ Stores
XG
X5 Group (Пятёрочка, Перекрёсток)Grocery Retail
✓ 3

X5 Group: AI Dynamic Pricing, Demand Forecasting, and Computer Vision Across 25,000+ Stores

Embedding AI across supply chain replenishment, dynamic pricing, and in-store operations generates tens of billions of rubles in verified annual EBITDA contribution for X5 Group.

25 000+stores running ML auto-replenishment and AI pricing
90%+of routine supplier orders generated autonomously by ML
20 000+self-checkout kiosks equipped with Computer Vision SKU recognition
+30%faster weighed-produce checkout speed via CV recognition
Dynamic Pricing MLCross-Elasticity ModelsEdge Computer Vision
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JPMorgan Chase: Rolling Out LLM Suite to 200,000 Employees with $1.5B+ in Annual AI/ML Business Value
JC
JPMorgan ChaseBanking & Investment Banking
✓ 3

JPMorgan Chase: Rolling Out LLM Suite to 200,000 Employees with $1.5B+ in Annual AI/ML Business Value

Roughly 200,000 JPMorgan employees (~two-thirds of the global workforce) gained access to LLM Suite, saving hours per week on document synthesis, memo drafting, and regulatory research.

200 000+bank employees onboarded to the internal LLM Suite platform
$1,5–2 млрдannualized business value across 400+ production AI/ML use cases
400+production AI use cases across fraud, risk, and investment banking
#1 в миреglobal ranking in the Evident AI Index for banking AI maturity
LLM Suite PortalPermission-Aware RAGMNPI Compliance Guardrails
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Walmart: Generative AI Enriched 850M Catalog Items 100x Faster While Autonomous Bots Negotiate Supplier Contracts
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WalmartGlobal Retail & Supply Chain
✓ 3

Walmart: Generative AI Enriched 850M Catalog Items 100x Faster While Autonomous Bots Negotiate Supplier Contracts

Enriching 850 million catalog items and launching generative intent search lifted e-commerce search relevance and basket size by turning multi-query shopping trips into single-prompt solutions.

850 млнproduct catalog listings enriched and standardized by GenAI
в 100 разfaster catalog enrichment compared to manual human workflows
68%of tail-spend suppliers reach agreement with the AI negotiation bot
75 000+corporate associates actively using the My Assistant AI platform
Wallaby Retail LLMGenerative Intent SearchCatalog Attribute Extrac…
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Dodo Brands: Computer Vision Pizza Quality Control and AI Scheduling in the Cloud Dodo IS
DB
Dodo Brands (Додо Пицца)FoodTech & QSR
✓ 3

Dodo Brands: Computer Vision Pizza Quality Control and AI Scheduling in the Cloud Dodo IS

Cut-table Computer Vision established continuous, objective quality scoring across hundreds of thousands of pizzas, reducing customer complaints regarding bake quality and topping errors by 25–30%.

1 300+restaurants across 20+ countries managed by the Dodo IS ML cloud
−28%drop in bake and topping quality complaints at CV-enabled stores
15 минgranularity of ML kitchen demand forecasting for shift scheduling
75%+of orders placed via digital channels with AI recommendations
Dodo IS CloudComputer Vision (YOLO / …Time-Series Demand ML
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Kaspersky: Streaming ML Pipeline Detects 467,000 New Daily Cyber Threats + Kira AI SOC Analyst
ЛК
Лаборатория Касперского (Kaspersky)Cybersecurity
✓ 3

Kaspersky: Streaming ML Pipeline Detects 467,000 New Daily Cyber Threats + Kira AI SOC Analyst

Kaspersky's automated ML pipeline detects and classifies roughly 467,000 novel malicious files daily (up 14% YoY), handling over 99.9% of incoming threat volume without human intervention.

467 000new unique malicious files detected daily by the ML pipeline
99.9%+of incoming threats classified automatically without humans
−40%reduction in SOC incident triage time using Kira AI assistant
400M+protected endpoints feeding the KSN threat intelligence grid
Behavioral ML EngineGradient Boosted TreesDeep Threat Clustering
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Severstal: 'Adelina' AI Operator Boosted Rolling Mill Throughput by 5% with Multi-Billion Ruble Impact
СS
Северсталь (Severstal)Metallurgy & Heavy Industry
✓ 3

Severstal: 'Adelina' AI Operator Boosted Rolling Mill Throughput by 5% with Multi-Billion Ruble Impact

The 'Adelina' AI controller increased Hot Strip Mill 2000 throughput by roughly 5% with zero capital equipment expansion—unlocking over 300,000 additional tonnes of rolled steel per year.

+5%throughput increase on Mill 2000 under 'Adelina' AI control
+300 тыс. тadditional tonnes of rolled steel per year with zero CapEx
20+ типовsurface defect classes detected at full line speed via CV
Автопилотclosed-loop autonomous PLC actuation directly from ML inference
Closed-Loop Industrial MLPLC / SCADA IntegrationCNN Surface Defect Vision
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VTB Bank: Industrial 'Model Factory' Cut ML Time-to-Market from Months to Days with Sub-Second Credit Scoring
БВ
Банк ВТБ (VTB)Banking & Finance
✓ 3

VTB Bank: Industrial 'Model Factory' Cut ML Time-to-Market from Months to Days with Sub-Second Credit Scoring

Transitioning to the industrial Model Factory slashed average ML time-to-market from several months to just days while cutting model development and validation labor costs by over 40%.

в 5–10 разfaster ML model deployment via the automated Model Factory
85%+of retail credit decisions executed automatically by AI
−40%reduction in engineering and risk-validation effort per model
Сотниproduction ML models monitored continuously for data drift
Enterprise MLOps FactoryCentralized Feature StoreGradient Boosting / GNN
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MegaFon: 'Eva' AI Assistant Blocks Billions of Spam Calls While ML Optimizes Base Station Energy & Traffic
МM
МегаФон (MegaFon)Telecom & Connectivity
✓ 3

MegaFon: 'Eva' AI Assistant Blocks Billions of Spam Calls While ML Optimizes Base Station Energy & Traffic

The 'Eva' AI assistant protects tens of millions of subscribers and blocks or intercepts billions of spam and scam calls annually—detecting newly rotated fraud numbers within minutes of campaign initiation.

Миллиардыspam and scam calls intercepted annually by 'Eva' AI
−10%base-station electricity savings via predictive ML sleep modes
200 000+base stations monitored by SON and predictive telemetry
65%+of subscriber support inquiries resolved autonomously by AI
Signaling Traffic MLConversational Voice AI …Self-Organizing Network …
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Drom.ru: Computer Vision and ML Price Millions of Vehicles and Detect Body Damage from Photos
ДD
Дром (Drom.ru / FarPost)Auto Classifieds & Marketplace
✓ 3

Drom.ru: Computer Vision and ML Price Millions of Vehicles and Detect Body Damage from Photos

Computer vision automatically verifies and tags millions of uploaded vehicle photos every month, slashing manual moderation workload while auto-filling make, model, and generation for sellers.

95%+accuracy identifying vehicle make, model & generation from photos
20M+monthly unique users relying on ML valuation and visual search
Доли секsub-second duplicate photo search across tens of millions of images
Сотниregional, trim, and history features inside the pricing model
Computer Vision (CNN / V…License Plate OCRVisual Embedding Search
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Klarna: the AI assistant did the work of 700 full-time agents in its first month
K
KlarnaFintech
✓ 3

Klarna: the AI assistant did the work of 700 full-time agents in its first month

In its first month the assistant handled 2.3 million conversations — two-thirds of all Klarna support chats — doing the equivalent work of 700 full-time agents. Average resolution time dropped from 11…

2.3Mchats in month one
700FTE equivalent
<2 минresolution (was 11 min)
$40Mprojected 2024 profit
OpenAI (ChatGPT API)RAG по документации подд…Белый список тем + шаг в…
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Airbnb: deep learning in search — +0.6% bookings from a new architecture and +14% bookings for new listings
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AirbnbTravel & Marketplaces
✓ 3

Airbnb: deep learning in search — +0.6% bookings from a new architecture and +14% bookings for new listings

The second wave's outcomes are fixed in online A/B tests. The two-tower architecture: +0.6% bookings and +0.75% revenue with −33% p99 scoring latency; a curious side effect — the average price of booked…

+0.6%bookings (two-tower NN)
+14%new-listing bookings
+1.8%revenue (position dropout)
-33%p99 scoring latency
GBDT → Deep NN (195 фич,…Lambdarank / ранжирующий…Two-tower архитектура (в…
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BBVA: 3 hours saved per employee per week, 20,000+ GPTs, and a ChatGPT rollout to 120,000 people
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BBVABanking
✓ 3

BBVA: 3 hours saved per employee per week, 20,000+ GPTs, and a ChatGPT rollout to 120,000 people

Per figures published by OpenAI in November 2025: about 3 hours saved per employee per week, 83% weekly active usage, efficiency improvements of up to 80%+ in tests of specific workflows, and 20,000+ custom…

~3 чsaved per employee per week
83%weekly active usage
20 000+custom GPTs (~4,000 in frequent use)
120 000employees in the target rollout across 25 countries
ChatGPT EnterpriseCustom GPTs (20 000+)OpenAI models (ассистент…
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Botkin.AI: what the rise and shutdown of Russia's first registered medical AI teaches
BA
Botkin.AI (ООО «Интеллоджик»)Healthcare
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Botkin.AI: what the rise and shutdown of Russia's first registered medical AI teaches

The outcome cuts both ways. The product ceased to exist as an independent business: regulatory suspension in November 2023, sale to a competitor in December 2023, out of the public eye. Investors who had…

2020Russia's first class 2b registration certificate for an AI platform
271+ млн ₽raised (11M in 2017 + 100M in 2019 + 160M in 2020)
150 000+studies in the Moscow pilot (March–December 2020)
−139 млн ₽loss in 2022 on 15.5M RUB revenue
Платформа Botkin.AI (КТ,…Интеграция с ЕРИС (46 ме…Статус медицинского изде…
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DoorDash: a Claude-powered voice AI fields hundreds of thousands of Dasher calls a day
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DoorDashDelivery
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DoorDash: a Claude-powered voice AI fields hundreds of thousands of Dasher calls a day

After successful testing in early 2024, DoorDash rolled the new self-service options out to all Dashers. The voice AI fields hundreds of thousands of Dasher calls daily at a response latency of 2.5 seconds…

100K+calls per day fielded by the voice AI
2.5сresponse latency (Claude 3 Haiku)
50×testing capacity increase (SageMaker)
8 недельfrom design to production A/B
Claude 3 Haiku (Anthropic)Amazon Bedrock + Knowled…Amazon Connect + Amazon …
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Duolingo: 148 new language courses in a year with generative AI
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DuolingoEdTech
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Duolingo: 148 new language courses in a year with generative AI

On April 30, 2025 Duolingo announced 148 new language courses at once — the largest content expansion in company history, created in under a year. The course catalog more than doubled. The seven most…

148new courses at once
<1 годаproduction time
12 летfor the first 100 courses
28interface languages
Generative AI (модели не…Shared content systemВнутренний тулинг генера…
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European Parliament: Archibot, a Claude-powered AI archivist — 80% less time searching 2.1M documents
ЕП
Европейский парламентGovernment
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European Parliament: Archibot, a Claude-powered AI archivist — 80% less time searching 2.1M documents

The case's key metric: document search and analysis time dropped by 80% while maintaining high accuracy and security standards. The system processes and provides access to more than 2.1 million documents in…

80%less search and analysis time
2,1M+documents made accessible
1952archive dating back to
Все ЕСEU member-state languages
Claude (Amazon Bedrock)Constitutional AIМногоязычный поиск и сум…
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Gazprom Neft: the 'Digital Rig' cut non-productive drilling time by 15%
ГН
Газпром нефтьOil & Gas
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Gazprom Neft: the 'Digital Rig' cut non-productive drilling time by 15%

Published results of the field trials at Gazpromneft-Noyabrskneftegaz: well construction time fell by 6 days versus norms (17.5% for complex wells), the speed of footage operations rose 28.5% and…

−15%complication-related non-productive time (program target; per participants — exceeded)
1,5+ чstick/slip warning before a forced stop
−6 сутокwell construction time in field trials (+28.5% footage-operation speed)
60% → 90%+horizontal-well placement efficiency in the pay zone (GeoNavigator center)
«Цифровая буровая» (6 те…IoT-датчики на буровом о…Автоматическая подача на…
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GitLab: Claude in GitLab Duo and internal workflows — 25–50% productivity gains
G
GitLabDevOps
✓ 3

GitLab: Claude in GitLab Duo and internal workflows — 25–50% productivity gains

Per Anthropic's case study, using Claude in internal workflows delivered 25–50% productivity gains at GitLab. AI feature development accelerated to "weeks, not years" — versus the scenario of building an…

25–50%internal workflow productivity gains
Неделиnot years to ship AI features
50M+registered GitLab users
50%of the Fortune 100 are customers
Claude 3 (семейство моде…GitLab Duo / Duo Agent P…Длинное контекстное окно
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Grab: LLM data classification — 20,000+ entities in the first month and 360 man-days saved per year
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GrabSuperapp: Ride-hailing & Delivery
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Grab: LLM data classification — 20,000+ entities in the first month and 360 man-days saved per year

In the first month after rollout the system scanned more than 20,000 data entities — averaging 300–400 per day, a pace physically unreachable for a manual process. In a September 2023 survey, 80% of data…

20K+entities in the first month
360man-days saved per year
80%of data owners found it helpful
300-400entities per day
GPT-3.5 (Azure OpenAI, к…Оркестратор Gemini (очер…Prompt engineering: few-…
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Harvey: a custom U.S. case law model that lawyers preferred over GPT-4 97% of the time
H
HarveyLegalTech
✓ 3

Harvey: a custom U.S. case law model that lawyers preferred over GPT-4 97% of the time

To test the model, Harvey brought in attorneys from 10 of the largest law firms. They were shown side-by-side outputs of the custom case law model and GPT-4 for the same question. "97% of the time, the…

97%lawyer preference vs GPT-4
10 млрдtokens of data added to the model
10xrevenue growth in 2023
$715MSeries B valuation ($80M raised)
Кастомно обученная модел…GPT-4 (бейзлайн)Прецедентное право США (…
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Instacart: Ava, the internal AI assistant — over half of employees monthly, with 20+ minute sessions
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InstacartE-grocery & Delivery
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Instacart: Ava, the internal AI assistant — over half of employees monthly, with 20+ minute sessions

As of the September 2023 post, over half of Instacart employees used Ava monthly and more than 900 weekly; sessions ran 20+ minutes, and users 'were producing and copying a significant amount of code'. The…

>50%of employees use it monthly
900+weekly users
20+ минsession length
32KGPT-4 context window in Ava
OpenAI GPT-4 (32K) и GPT…Автоапгрейд моделей по р…Prompt Exchange (библиот…
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Intercom Fin: a Claude-powered support agent resolving up to 86% of questions
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IntercomSaaS / Customer support
✓ 4

Intercom Fin: a Claude-powered support agent resolving up to 86% of questions

Out of the box Fin resolves 51% of inquiries on average; tuned for a specific company, resolution reaches 86% — a gap that itself shows knowledge-base quality is decisive. Response times drop from ~30…

86%max Fin resolution
51%out-of-the-box resolution
45+languages
+31%agent conversations/day (Lightspeed)
Claude (Anthropic)Fin AI Engine™ (6 слоёв,…fin-cx-retrieval / fin-c…
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LinkedIn: LLM-based skills extraction — 200 profile edits per second with a model 80% smaller at no quality loss
L
LinkedInHR Tech & Professional Networks
✓ 2

LinkedIn: LLM-based skills extraction — 200 profile edits per second with a model 80% smaller at no quality loss

In production the system handles ~200 profile edits per second at up to 100 ms per message — on CPU, thanks to a distilled model 80% smaller than the original. In online A/B tests, improved skills…

200/секprofile edits processed per second
-80%model size (distillation)
<100 мсper message
41K+skills in the taxonomy
Multilingual BERT (two-t…Transformer contextual e…Мультизадачный кросс-дом…
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Lonely Planet: travel itinerary generation with Claude on Amazon Bedrock — 80% cheaper than manual curation
LP
Lonely PlanetTravel & media
✓ 5

Lonely Planet: travel itinerary generation with Claude on Amazon Bedrock — 80% cheaper than manual curation

The headline public figure: itinerary generation costs dropped by roughly 80% versus manual curation. The platform creates thousands of unique travel itineraries, each of which previously took the team days…

80%cheaper than manual curation
Минутыper itinerary, down from days
270K+mappable destinations
750+local experts in the knowledge base
Anthropic Claude 2Amazon BedrockRAG по контентным библио…
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Mercado Libre: the GPT-4o-powered Verdi platform — a 100x catalog expansion and nearly 99% fraud-flag accuracy
ML
Mercado LibreE-commerce & Fintech
✓ 2

Mercado Libre: the GPT-4o-powered Verdi platform — a 100x catalog expansion and nearly 99% fraud-flag accuracy

Per figures published by OpenAI: GPT-4 Vision enabled cataloging 100x more products within two years; fraud-detection accuracy reached nearly 99% for flagged items; within a few months Verdi took over 10%…

100xmore products cataloged in 2 years
~99%fraud-detection accuracy (flagged items)
10%of mediations handled by AI on a major site
$450Mpotential in autonomous decisions annually
GPT-4o / GPT-4o mini / G…GPT-4 VisionVerdi (внутренняя платфо…
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Moderna: 750 internal GPTs in two months and 120 AI conversations per employee per week
M
ModernaPharma & Biotech
✓ 3

Moderna: 750 internal GPTs in two months and 120 AI conversations per employee per week

Within two months of adopting ChatGPT Enterprise, the company had 750 GPTs, 40% of weekly active users had created their own GPTs, and each user averaged 120 ChatGPT Enterprise conversations per week —…

750GPTs in the first 2 months
120conversations per user per week
40%of weekly actives created GPTs
100%adoption in the legal team
ChatGPT EnterpriseOpenAI API (mChat)Custom GPTs (Dose ID, Co…
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Morgan Stanley: the GPT-4 assistant used by 98% of financial advisor teams
MS
Morgan StanleyFinance
✓ 5

Morgan Stanley: the GPT-4 assistant used by 98% of financial advisor teams

More than 98% of financial advisor teams use the assistant — near-ceiling adoption for a corporate tool. The system effectively answers questions across a corpus of 100,000 documents — versus roughly 7,000…

98%advisor teams adoption
100Kdocuments in corpus
20→80%document accessibility
7K→100Kquestion coverage
GPT-4 (OpenAI)RAG по внутренней базе з…Систематические evals + …
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M.Video-Eldorado: in-store video analytics — an honest look at a pilot, not a 'chain-wide rollout'
МВ
М.Видео-ЭльдорадоRetail
✓ 7

M.Video-Eldorado: in-store video analytics — an honest look at a pilot, not a 'chain-wide rollout'

This case's results exist in two genres, and it is important not to mix them.

1 → 50stores: pilot (Oct 2020) → scaling plan (Aug 2021)
25 → 5'lonely shopper' alerts per day over 1.5 months
~$50edge device per camera (Raspberry; RTSP filter: 3 Mbit/s per 30 cameras)
30 000cameras — target scale (1,000+ stores)
Собственная CV-система д…~30 IP-камер на магазин …Edge-устройства Raspberr…
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Netflix: the recommender system drives 80% of viewing and saves more than $1B a year
N
NetflixMedia & Streaming
✓ 2

Netflix: the recommender system drives 80% of viewing and saves more than $1B a year

The recommender system is used on most screens of the product and in total influences choice for about 80% of hours streamed on Netflix; the remaining 20% comes from search. The figure's evolution is…

>$1Bsaved per year
80%of streamed hours via recommendations
4xeffective catalog size
65M+members at publication
Personalized Video Ranke…Top-N Video RankerTrending Now
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Notion: Claude-powered agents inside the workspace cut customers' information search time by 35%
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NotionProductivity software
✓ 3

Notion: Claude-powered agents inside the workspace cut customers' information search time by 35%

The impact is measured primarily on the side of Notion's customers. Osaka Gas cut information search time by 35%. Remote saves 10 minutes per search across 300 daily queries. dbt Labs saved over $35k a year…

35%less search time (Osaka Gas)
10 минsaved per search × 300 daily queries (Remote)
90%cost reduction (prompt caching)
30+concurrent agent tasks
Claude Opus 4.6Claude Managed AgentsPrompt caching
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Oscar Health: documentation nearly 40% faster, claims escalations resolved 50% faster, and the first BAA with OpenAI
OH
Oscar HealthHealth Insurance
✓ 3

Oscar Health: documentation nearly 40% faster, claims escalations resolved 50% faster, and the first BAA with OpenAI

Per the OpenAI case study (April 2024): time spent documenting care conversations and reviewing lab results fell by nearly 40% (per the company blog — from 20+ minutes to under 12 per conversation); the…

~40%faster clinical documentation (20+ min → <12 min)
50%faster claims escalation resolution
100% / 80%assistant accuracy by question category (simple / hardest)
№1first insurer BAA with OpenAI
OpenAI APIGPT-4BAA / HIPAA-контур
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Ozon: a transformer in search suggestions — honest fractions of a percent at trillion-ruble scale
O
OzonE-commerce
✓ 6

Ozon: a transformer in search suggestions — honest fractions of a percent at trillion-ruble scale

Published gains by iteration: search-suggestion CTR +10%, then another +10% and +3%; the share of empty result pages fell 3%; the share of users finishing a session with an order grew 0.3%. Every figure…

+0,3%users finishing a session with an order
+10%suggestion CTR in iteration one (then +10% and +3%)
−3%share of empty search results
300 мсresponse budget at tens of thousands of RPS
Decoder-only трансформер…Pretrain на цепочках пол…TensorRT-LLM (инференс),…
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Palo Alto Networks: Claude for thousands of developers — feature development velocity up 20–30%
PA
Palo Alto NetworksCybersecurity
✓ 2

Palo Alto Networks: Claude for thousands of developers — feature development velocity up 20–30%

The headline numbers agree across both case studies: feature development and code implementation velocity rose 20–30%. Unit test writing speed increased 10–30% — and that is not just productivity: more…

20–30%feature development velocity
10–30%faster unit test generation
70%faster task completion for juniors
Неделиonboarding, down from months (up to 6)
Claude 3.5 Haiku (автодо…Claude 3.5 Sonnet (чат в…Sourcegraph Cody (IDE-пл…
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Pfizer: generative AI on AWS — 16,000 search hours saved yearly and impact estimated at up to $1B annually
P
PfizerPharma
✓ 3

Pfizer: generative AI on AWS — 16,000 search hours saved yearly and impact estimated at up to $1B annually

The program's measured results: up to 16,000 search hours saved annually for 1,500 PSSM scientists and a 55% reduction in infrastructure costs (per the AWS case study). Prototypes ship in 6 weeks instead of…

16 000 чsearch hours saved yearly
$750M–1Bestimated annual savings (priority use cases)
55%infrastructure cost reduction
6 недельprototype, down from 3+ months
Anthropic Claude 2.1 (Am…Amazon KendraПлатформа Vox (голос + ч…
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Pinterest: one embedding instead of three — +46.7% repins in Lens and a third of the ML systems to maintain
P
PinterestSocial & Visual Search
✓ 3

Pinterest: one embedding instead of three — +46.7% repins in Lens and a third of the ML systems to maintain

In offline human-judgment evaluations the unified embedding lifted precision@5 by +22.2% for Flashlight, +110.1% for Lens, and +72.1% for Shop-the-Look versus the specialized models. The hardest domain…

+46.7%repins in Lens (A/B)
+110%precision@5 in Lens
600M+visual searches per month
3→1embeddings to maintain
Multi-task metric learningSE-ResNeXt101 (ImageNet …Модуль сабсэмплинга (про…
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Replit: a Claude-powered agent turned the platform into a growth machine — ARR from $1M to $240M
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ReplitSoftware development
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Replit: a Claude-powered agent turned the platform into a growth machine — ARR from $1M to $240M

The headline number of Anthropic's case study: Replit's annual recurring revenue grew from $1M to $240M on the back of Claude-powered agent launches. The chronology from independent sources matches in order…

$1M→$240MARR growth
50M+platform users
6+ чautonomous agent runtime
36 000+lines of code in one session
Claude Sonnet 4.6Claude Opus 4.7Replit Agent 4 (+ цифров…
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Rosatom: repair optimization gave Russian NPPs 94 extra operating days in half a year
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Росатом / РосэнергоатомNuclear power
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Rosatom: repair optimization gave Russian NPPs 94 extra operating days in half a year

In the first half of 2026, repair campaigns across the Russian NPP fleet were shortened by a combined 94 days (maintenance ran on 17 power units), and output, per the June 16, 2026 forecast, exceeded plan…

−94 дняcombined fleet-wide repair reduction (H1 2026, 17 units)
7,44 млрд кВт·чextra output above plan (H1 2026)
−30%diagnostics time in digital-twin pilots
240 → 100 минmaintenance data-exchange cycle at Rostov NPP (851 hours saved a year)
Виртуально-цифровая АЭС …Цифровые двойники оборуд…Цифровые двойники АСММ (…
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Russian Post: OCR and robots sort around 8 million mail items a day
ПР
Почта РоссииPostal & logistics
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Russian Post: OCR and robots sort around 8 million mail items a day

The automated pipeline sustains a national-scale flow. Confirmed operational figures: 40,000 letters per hour on the letter-sorting machine (11 per second), up to 8,000 parcels per hour on the parcel…

~8 млнitems per day (2.6B letters + 400M parcels a year)
11/секletters per second on the sorting machine (40,000/hour)
3 млн/деньVnukovo center capacity (64,000 m², 125,000 letters/hour)
4 секper parcel for the robotic manipulator
OCR адресов и индексов (…Сортировочные машины Tos…SMAB — собственная платф…
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SK Telecom: a telco LLM on fine-tuned Claude — 68% fewer low-quality support responses
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SK TelecomTelecom
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SK Telecom: a telco LLM on fine-tuned Claude — 68% fewer low-quality support responses

LLM response quality ratings from live agents rose 34% (in-call assistance). The share of low-quality responses fell 68% — the fine-tuned telco model versus the base model. In post-call processing, quality…

−68%low-quality model responses
+34%response quality ratings
89%of human-agent quality (post-call)
3,3→4,3+telco LLM overall score
Claude 3 Sonnet (fine-tu…Bedrock Knowledge Bases …In-Call Assist + Post-Ca…
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Snapchat: multimodal Gemini in the My AI chatbot — over 2.5x engagement growth in the U.S.
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Snap Inc.Social Media
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Snapchat: multimodal Gemini in the My AI chatbot — over 2.5x engagement growth in the U.S.

Per Snap and Google Cloud, after deploying Gemini in My AI, engagement within Snapping to My AI grew by over 2.5x in the United States. Kurian's wording in the Google blog: "Snap deployed the multimodal…

2.5x+My AI engagement growth (U.S.)
850M+Snapchat monthly active users
5modalities: text, audio, image, video, code
2024Gemini launch in My AI (September)
Gemini (Google Cloud)Vertex AIMy AI (Snapchat)
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Spotify: an LLM-powered annotation platform — a 10x larger labeling corpus with 3x annotator productivity
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SpotifyMedia & Music
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Spotify: an LLM-powered annotation platform — a 10x larger labeling corpus with 3x annotator productivity

The annotation corpus grew 10x and annotator productivity 3x: automating sampling, review serving, and feeding results back into training pipelines removed the manual steps, while LLM labeling took the bulk…

10xannotation corpus growth
3xannotator productivity
100M+tracks & episodes in catalog (hundreds of millions)
10+parallel labeling projects (dozens)
LLM-разметка параллельно…3 уровня человеческой эк…Метрики согласованности …
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Stripe Radar: the anti-fraud neural network decides in under 100 ms and wrongly blocks just 0.1% of legitimate payments
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StripeFintech
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Stripe Radar: the anti-fraud neural network decides in under 100 ms and wrongly blocks just 0.1% of legitimate payments

Radar makes a decision on every payment in less than 100 milliseconds — inside the payment flow, before the transaction is confirmed. Out of billions of legitimate payments on Stripe, the system incorrectly…

<100 мсdecision per transaction
0.1%of legit payments wrongly blocked
1000+signals per transaction
-85%training time (to <2 hours)
DNN (multi-branch, ResNe…Ранее: XGBoost + Wide & …1000+ фич на транзакцию
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Uber QueryGPT: natural language to SQL — 10 minutes down to 3
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UberTechnology
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Uber QueryGPT: natural language to SQL — 10 minutes down to 3

QueryGPT produces sufficiently reliable queries in about 3 minutes versus ~10 minutes of manual authoring — roughly 70% time saved per query. In limited release the service averages about 300 daily active…

10→3 минquery authoring time
1.2Mplatform queries/mo
300daily active users
78%report time savings
GPT-4 Turbo 128K (модель…LLM-конвейер агентовRAG + vector search (kNN)
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Vodafone: Copilot for 68,000 employees — ~3 hours saved weekly, while TOBi resolves 70% of ~45M monthly inquiries
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VodafoneTelecom
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Vodafone: Copilot for 68,000 employees — ~3 hours saved weekly, while TOBi resolves 70% of ~45M monthly inquiries

The Copilot pilot, assessed with KPMG, produced the case's headline number: savings of around three hours per week per person on average — on emails, minutes, and information search. 90% of participants…

68 000employees getting Copilot
~3 чsaved per person weekly (pilot)
70%of inquiries TOBi resolves digitally
−1 минoff average call time (SuperAgent)
Microsoft 365 CopilotAzure OpenAI ServiceAzure AI Foundry
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Wayfair: Gemini enriches a 30M+ product catalog 67% faster and lifts filter conversion by ~2%
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WayfairRetail (E-commerce)
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Wayfair: Gemini enriches a 30M+ product catalog 67% faster and lifts filter conversion by ~2%

The published numbers focus on the catalog front. Per the joint Wayfair–Google Cloud press release: the time needed to curate new and update existing product listings dropped by 67% across a catalog of more…

67%faster listing curation
30M+products in the catalog
~2%conversion lift with filters
$100K+annual savings (hundreds of thousands)
Gemini (Google Cloud)Vertex AI / Gemini Enter…Muse + вкладка Discover …
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Wildberries: AI in marketplace operations — warehouse machine vision, neural search, and review processing
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WildberriesE-commerce
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Wildberries: AI in marketplace operations — warehouse machine vision, neural search, and review processing

Wildberries still discloses almost no quantitative business results for its AI systems — the case's key honest limitation. What is published are technical indicators: robot-arm productivity of at least 950…

15 млнorders per day (company data, Habr)
950/часrobot-arm units/hour, >97% grip rate (TAdviser)
1–3 годаpublic goal: radically reduce dependence on warehouse staff
Шестиосевые роботы-манип…Goods-to-person роботы-п…Нейросетевой поиск и под…
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Yandex: Alice processes smart-home commands on the speaker itself — on average 6x faster than the cloud
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ЯндексSmart home
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Yandex: Alice processes smart-home commands on the speaker itself — on average 6x faster than the cloud

According to the Yandex team, local processing of smart-home commands is on average 6x faster than the cloud path — the gain comes from eliminating the network round trip plus streaming on-device…

6×local processing faster than cloud (on average)
~10 млнparameters in the on-device ASR model
90 МБof RAM for the 'Begemotik' NLU (30+ GB in the cloud)
5,3 млнactive YaOS/YaOS X devices (2025)
SoC Amlogic A113X2 с NPU…On-device ASR: трансформ…On-device NLU «Бегемотик…
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