美团CatPaw定名“妙手” AI正在本地生活经营场景加速落地

This article covers Meituan’s official launch of "MiaoShou" (literally "skilled hand"), its AI tool formerly codenamed CatPaw. Designed as a practical AI solution for local business operations, it offers valuable insights for both readers following local services industry trends and small business owners running brick-and-mortar stores.

1. Unlike general office AI tools focused on copy generation or document organization, MiaoShou is integrated with Meituan’s full-stack data across catering, hotel and travel, healthcare, and instant retail, covering consumer demand, transactions, reviews, and order fulfillment. It supports users through the entire workflow from identifying operational issues to making decisions and executing actions.

2. Built on an architecture of a unified workbench paired with industry-specific AI modules, the tool centralizes request intake, task breakdown, and permission management, while also delivering tailored functions for different sectors, including operational analysis, negative review management, and multi-store inspections, to meet the operational needs of diverse business formats.

3. Two useful frameworks can help readers evaluate the value of practical AI tools: first, a qualified solution must meet three core criteria—targeting high-value scenarios, leveraging high-quality data, and enabling closed-loop execution; second, AI implementation should follow the "four highs" pathway: driving efficiency in high-repeat workflows, improving quality in high-investment processes, boosting volume in high-return segments, and accelerating pilots for new initiatives, to avoid paying for flashy but unproductive AI concepts.

This article covers Meituan’s official launch of "MiaoShou", its vertical AI operational tool for the local services sector, formerly codenamed CatPaw. For brands across categories operating local life channels, the tool represents an important new piece of infrastructure to improve channel operation efficiency and optimize customer management, with key takeaways as follows:

1. MiaoShou integrates the platform’s full-stack operational data across catering, service retail, healthcare, hotel and travel, and instant retail. It can accurately identify differences in customer demographics, product fit, and peak operating hours across different business districts for brands, while rapidly responding to unexpected operational disruptions such as negative reviews, unplanned store closures, and sudden weather changes. This addresses a core pain point for scaled brands: balancing standardized operational requirements with location-specific adaptation for individual stores.

2. Built on an architecture combining a unified workbench and vertical industry AI modules, the product supports concrete operational tasks including performance analysis, review management, and multi-store inspections. Designed with the core metric of "understanding specific business contexts and delivering tangible operational results", it significantly lowers the cost for brands to access professional data analysis and business consulting capabilities.

3. When building in-house AI operational capabilities, brands can reference the "four highs" implementation framework outlined in the *MiaoShou Catering AI Operation Guide*: drive efficiency in high-repeat workflows, improve quality in high-investment processes, boost volume in high-return segments, and accelerate rollout of new pilot initiatives, to steadily advance digital transformation.

Meituan has officially launched "MiaoShou", its AI operational tool for local services formerly codenamed CatPaw. Designed as a new efficiency-driving tool for local sellers across all categories, it helps sellers of all scales reduce operating costs and improve operational efficiency, with key practical takeaways as follows:

1. MiaoShou addresses a key limitation of general office AI tools, which fail to adapt to the highly scenario-specific nature of local business operations. With seller authorization, it can access the platform’s full-stack data covering consumer demand, transactions, reviews, and fulfillment, enabling a closed-loop workflow from problem identification to decision-making and execution, covering high-frequency needs from negative review response and store inspections to operational analysis.

2. The tool is adapted for sellers of all scales: large chain operators can use it to resolve the tension between standardized management and location-specific operational flexibility, while small and medium-sized sellers can access professional-grade operational guidance without bearing the high cost of hiring specialized teams or purchasing data analysis systems.

3. When selecting and deploying AI tools, sellers can reference the three core criteria for AI value proposed by Wang Puzhong, CEO of Meituan Core Local Commerce: high-value scenarios, high-quality data, and closed-loop execution, to avoid conceptual products that only generate copy but cannot solve practical problems. They can also identify efficiency improvement opportunities following the "four highs" pathway outlined in the *MiaoShou Catering AI Operation Guide*, prioritizing AI deployment in high-repeat, high-investment, high-return, and new pilot scenarios to deliver tangible operational results.

Meituan’s launch of "MiaoShou", its local services AI tool, creates new business opportunities and provides a digital transformation reference for manufacturing factories expanding into instant retail and local life channels, with key practical insights as follows:

1. Local life channel operations are highly location- and time-sensitive: product preferences, assortment structure, and sales turnover for the same category can vary dramatically across business districts, while factors such as negative reviews, weather changes, and store operating status can rapidly impact product sales. Leveraging MiaoShou’s integrated full-stack data on consumption, transactions, reviews, and fulfillment, factories can more accurately capture regional user product preferences, to inform dynamic adjustments to product design, production planning, and inventory allocation, reducing inventory waste and improving sell-through efficiency.

2. Built on a "unified workbench + industry-specific AI module" architecture, MiaoShou automates tasks including operational analysis, channel inspection, and user feedback management. When expanding into local life channels, factories can access professional operational capabilities at low cost without building expensive dedicated operations and data analysis teams, lowering the barrier to new channel entry.

3. Factories advancing digital transformation can reference the "four highs" implementation pathway, prioritizing AI deployment in high-repeat production and supply processes, high-investment supply chain links, high-return core category segments, and new channel pilots, to steadily deliver tangible operational results.

Meituan’s official launch of "MiaoShou", its vertical AI product for local services, marks the entry of AI in local business B2B services into the closed-loop implementation stage, and provides clear references for industry trends, core customer pain points, and actionable solutions for various merchant service providers, with key takeaways as follows:

1. In terms of industry trends and customer pain points, general office AI tools can no longer meet the real needs of local businesses: local operations are heavily influenced by time, location, and scenario, with significant differences in customer groups, product offerings, and operating hours across business districts, while variables such as negative reviews, sudden weather changes, and unplanned store closures can rapidly impact performance. Large chain brands struggle to balance standardization and flexible operations, while small and medium-sized merchants cannot afford professional service costs, creating strong demand for practical AI services.

2. A reference solution follows a three-layer architecture: the bottom layer integrates high-quality full-stack data covering consumption, transactions, supply, reviews, and fulfillment; the middle layer builds a unified AI workbench to intake requests, break down tasks, and manage permissions; the top layer deploys vertical functional modules by industry to enable closed-loop execution functions including operational analysis, review management, and multi-store inspections.

3. When developing AI products, service providers should anchor on the three core criteria proposed by Wang Puzhong—high-value scenarios, high-quality data, and closed-loop execution—to avoid building conceptual products, and can design service scenarios following the "four highs" pathway to structure their offerings.

Meituan’s launch of "MiaoShou", its AI operational tool for local services, provides a latest reference practice for merchant services and operation management across local life and instant retail platforms, with key takeaways as follows:

1. Platforms currently face three core pain points in serving merchants: general office AI tools cannot adapt to the highly time-, location-, and scenario-specific nature of local business operations, making it difficult to solve real problems; large chain merchants struggle to balance standardized management with the need for flexible, location-specific store operations; small and medium-sized merchants cannot afford the cost of professional data analysis and business consulting, facing high barriers to digitalization. These pain points are core entry points for platforms to improve merchant stickiness.

2. Meituan’s latest practice is the unified MiaoShou AI workbench, which, with merchant authorization, integrates the platform’s accumulated full-stack data on consumption, transactions, supply, reviews, and fulfillment to centralize request intake, task breakdown, and permission management. It also deploys dedicated AI modules for different sectors including catering, hotel and travel, and healthcare—such as the Smart Shopkeeper and Kangaroo Manager modules for catering, and the "JiBai" module for other sectors—to deliver closed-loop services including operational analysis, review management, and multi-store inspections, with the core success metric of helping merchants achieve tangible operational results.

3. When building AI-powered merchant services, platforms should adhere to the three principles of high-value scenarios, high-quality data, and closed-loop execution, to avoid launching flashy but unproductive conceptual features, and can advance feature iteration steadily following the "four highs" pathway.

Meituan’s official naming of its formerly codenamed CatPaw AI product as "MiaoShou" marks the in-depth implementation of AI technology in offline local business scenarios. As a notable new trend in the integration of digital technology and the real economy, the development carries high research value, with key points of interest as follows:

1. AI applications in the local services sector are showing a new evolutionary direction: general office AI tools limited to copy generation and document organization cannot adapt to the strong spatiotemporal dependence and scenario-specific nature of local business operational needs. What the industry truly needs is vertical AI operational experts that understand industry rules, connect to full-stack operational data, and can invoke system actions to execute tasks within authorized scope, representing a shift in AI applications from "answering questions" to "solving problems".

2. New business model exploration is emerging in the local services space: Meituan’s architecture of a "unified AI workbench + cross-industry vertical AI expert panel" is designed to translate the platform’s accumulated consumer data, operational knowledge, and service methodologies into AI capabilities that merchants can use, train, and manage independently, ultimately helping merchants build their own in-house AI operational capabilities. This model may restructure the digital service ecosystem for local businesses.

3. The three core criteria for AI value proposed by Meituan, and the "four highs" scenario screening pathway outlined in the *MiaoShou Catering AI Operation Guide*, provide a practical analytical framework for AI implementation across more real-economy industries.