千峰对话：To B智能体更需要长期精准记忆

This cutting-edge industry dialogue offers a quick overview of the core development logic and practical value of the AI memory sector, along with actionable, up-to-date industry insights.

1. Key player updates: Red Bear AI, a startup focused on AI memory technology, was founded in April 2024. It has closed six funding rounds in just 15 months, reaching a post-money valuation of nearly RMB 3 billion and serving over 500 enterprise clients. Its proprietary memory engine ranks first globally in two core long-memory benchmarks for AI—cross-conversation memory continuity and ongoing conversation memory accuracy—with retest scores of 91.54% and 95% respectively.

2. Key conceptual update: B2B AI agents have far stronger demand for long-term, accurate memory than consumer-facing applications. For C-end use cases, memory only needs to deliver the pleasant surprise of being remembered; for B-end scenarios, however, memory must meet four strict criteria: permanent storage without data loss, automatic structuring of unstructured content into knowledge bases, traceable and compliant outputs, and one-click correction of content biases. These requirements are difficult for general-purpose large language models (LLMs) to fulfill in real-world deployments.

3. Practical scenario value: AI agents equipped with long-term memory capabilities can accurately identify user consumption cycles, run private-domain user engagement through personified brand characters, and deliver far higher conversion rates than traditional customer service. This is currently an effective solution for brands struggling with low return on investment (ROI) from public-domain traffic advertising.

This dialogue provides clear, actionable guidance for brands looking to solve traffic bottlenecks, implement AI-powered marketing and private-domain operations, and make informed AI tool selections.

1. Align with consumption channel trends: Widespread ROI inversion is now common in public-domain traffic advertising: an RMB 1 million ad spend often yields only RMB 800,000 in platform-attributed revenue, which easily turns into a loss after fulfillment and labor costs. The share of private-domain e-commerce will continue to rise—Sam’s Club now generates over 50% of its e-commerce revenue through its own channels, and IKEA China hits 25%, both clear signals of this shift. The long-term endgame for brands is to unlock private-domain value through strong user loyalty.

2. Understand the tangible value of AI operations: AI agents with long-term, accurate memory can recall user consumption habits and product use cycles, interact with customers through brand-specific personified roles such as health consultants or personal stylists, and deliver tailored offers right at users’ repurchase windows. These agents deliver far higher conversion rates than traditional customer service, and can effectively activate large volumes of dormant private-domain users.

3. Avoid common AI selection pitfalls: General-purpose LLMs are inherently designed for consumer use cases, and can only deliver roughly 70% of desired performance when deployed in enterprise scenarios, with extremely high costs for subsequent optimization. They also struggle to meet enterprise compliance requirements such as on-premises data storage, traceable memory, and editable outputs. Memory layer tools that are compatible with any LLM and support private deployment are a far better fit for brands’ actual needs.

This dialogue offers actionable operational guidance and risk warnings for sellers tackling growth challenges from rising traffic costs and low private-domain conversion rates.

1. Capture high-certainty growth opportunities: Continuously declining ROI on public-domain traffic investment is a universal trend across all product categories. Driving traffic from public channels and driving repeat purchases in private domains has become the core profit model for categories including apparel, anti-hair loss hair care, and dietary supplements. Categories with inherent repeat purchase cycles are particularly well-suited for private-domain operations, and the rising share of private-domain e-commerce is a clear, long-term industry trend.

2. Adopt high-efficiency operational practices: A single human customer service representative can manage at most 150 private-domain users, with the average being only around 50—far too few to cover the thousands or even tens of thousands of private-domain users accumulated by most stores. AI agents with long-memory capabilities can accurately recall users’ purchase history, product use cycles, and preferences, interact with customers in personified roles such as stylists or category consultants, and push targeted offers at repurchase nodes, delivering far higher conversion than traditional scripted customer service.

3. Mitigate operational risks: Current general-purpose LLMs deployed in customer service and marketing scenarios suffer from opaque, uncontrollable memory "black boxes", untraceable outputs, and high costs for content error correction. When selecting AI tools, prioritize solutions that support private storage of memory data, traceable outputs, and one-click correction of deviations to avoid compliance risks and brand reputation damage.

This content provides clear direction for factories pursuing digital transformation, identifying AI-related business opportunities, and reducing the cost of digital implementation.

1. Identify real digital demand: For both brand clients and industrial B2B players, the core requirement for AI applications is not flashy technology, but practical problem solving. For example, customer service and marketing scenarios need long-term, accurate user memory capabilities, while procurement and supply chain management require reliable, long-term memory for hundreds of thousands of supplier and product datasets. Most enterprises face extremely high costs if they build such systems in-house, creating significant demand for third-party solutions.

2. Unlock new business opportunities: Long-memory AI capabilities are becoming a foundational component for all types of AI agents. The traditional model of software development may change fundamentally in the future: instead of relying on extensive hard-coded front-end development and database connections, full business workflows can be delivered by agents calling standardized skill components. As factories expand into digital business lines, they can explore new growth opportunities around the integration and implementation services for these standardized components.

3. Learn best practices for digital tool selection: Factories implementing AI digital tools should not blindly chase general-purpose LLMs, which are designed primarily for consumer use cases and typically only deliver roughly 70% of required performance in industrial scenarios, with very high follow-up optimization costs. Instead, prioritize practical tools that are compatible with multiple model backends, support on-premises data storage, and deliver traceable, editable outputs to reduce total implementation costs.

This dialogue clearly outlines industry trends, real client pain points, and differentiated technology roadmaps for B2B AI services, offering high-value reference for service providers’ business planning.

1. Align with industry development trends: Enterprise AI deployment is shifting from general-purpose model API calls to deep, scenario-specific implementation. Long-term, accurate memory capabilities are in high demand across three core directions: AI-powered operations, AI-enabled products, and AI-driven business models. In the future, long memory will become a mandatory foundational component for all industrial AI agents, with far higher memory requirements than consumer-facing applications.

2. Address core enterprise client pain points: Enterprises currently face four key bottlenecks when deploying AI: First, general-purpose LLMs have opaque memory black boxes, where stored content and correction actions are completely uncontrollable; second, common add-on memory modules essentially rely on keyword matching for storage and retrieval, and cannot enable human-like memory-driven reasoning; third, integrated cloud-plus-model solutions from large tech vendors create strong lock-in, raising the barrier for clients to switch models and reducing control over memory data; fourth, general-purpose models cannot meet enterprise compliance requirements for traceable data, editable outputs, and automated knowledge base structuring.

3. Adopt proven solution approaches: The independent, portable memory engine roadmap—which supports integration with any LLM, meets private deployment requirements, and packages memory capabilities as standardized skill components for various agents to call—is a market-validated differentiated path. Red Bear AI has acquired more than 500 enterprise clients in 15 months using this approach, with industry-leading core memory performance metrics.

This content provides cutting-edge reference for platforms to understand merchant needs in the AI era, adjust platform service directions, and avoid structural development risks.

1. Recognize core platform challenges: ROI inversion on public-domain traffic investment has become a widespread pain point for brands and sellers: returns from ad spend often fail to cover input costs after accounting for fulfillment and labor expenses. A growing number of brands are building their own private-domain channels—leading players like Sam’s Club and IKEA already generate 25% to 50% of their e-commerce revenue through private channels. Public-domain platforms now face declining merchant willingness to invest and rising difficulty retaining users.

2. Clarify merchants’ demand for platform AI services: Merchants have strong demand for long-memory AI capabilities across user operations, customer service, and supply chain management. They need AI that can accurately remember user characteristics, consumption cycles, and historical supplier data to deliver traceable, editable, compliant outputs—requirements that current general-purpose models struggle to meet in these vertical scenarios.

3. Adjust strategy to mitigate risks: If platforms build AI ecosystems around tightly coupled, locked-in cloud-plus-model solutions that deliberately raise switching costs for merchants changing model backends, they will leave room for independent memory engine startups to compete on differentiation. Platforms can instead consider opening up their ecosystems, integrating compatible memory capability components to help merchants improve operational conversion across both public and private domains, and retain core merchant resources.

This cutting-edge dialogue documents multiple new trends, practical challenges, and innovative business models in the industrial implementation phase of AI, holding high reference value for industry research.

1. Notable new industry trends: The core competitive differentiator for B2B AI agents is shifting from general model capabilities to long-term, accurate memory capabilities. Long-memory components are evolving from model-embedded features and add-on storage modules into an independent, portable middle layer of dedicated memory engines. The fundamental form of software may also change, as traditional hard-coded development is gradually replaced by workflows completed by agents calling standardized skill components. Meanwhile, the share of private-domain e-commerce continues to rise, and long-memory AI is becoming a core tool for brands to unlock private-domain value.

2. Emerging challenges in industry development: Large tech companies face structural contradictions between their R&D mechanisms and business models: on one hand, they will not allocate full resources to deeply develop vertical long-memory technology, as R&D teams are typically incentivized to meet KPIs rather than achieve deep technical breakthroughs; on the other hand, their integrated cloud-plus-model ecosystems are reluctant to open up portable memory layers, as that would lower switching costs for clients, leaving general-purpose LLMs consistently unable to match real enterprise needs. In addition, China’s domestic AI ecosystem remains underdeveloped, and mature products are easily copied after launch, eroding the return on investment for innovative firms.

3. Representative innovative business model case: Red Bear AI centers its offering on memory technology, focuses on B2B customer service and marketing scenarios, and builds its memory engine as a foundational component compatible with any LLM that supports private deployment, while expanding into AI CRM and planning future C-end applications. Its growth trajectory—closing six funding rounds in 15 months and serving over 500 clients—makes it a typical research case for vertical AI application implementation.