没有记忆 AI推理落不了地｜爱分析访谈

This article is an interview with the founder of Red Bear AI, an AI startup, that shares insights on the AI memory track. Key takeaways are as follows:

1. A core flaw of most existing AI applications is incorrect logical ordering: they implement memory as an external patch to large language models, adding memory after inference, which leads to repeated questioning of users in customer service and after-sales scenarios, low accuracy, and frequent hallucinations. Red Bear AI's human-like memory engine follows a "retrieve memory first, inference second" logic, which effectively reduces token costs, cuts down factual hallucinations, and improves response accuracy.

2. Instead of following the common path of developing underlying technology first and waiting for market maturation, Red Bear AI chose to start with monetizable scenarios such as customer service, marketing and after-sales, using application revenue to fuel iterations of its underlying technology. This path has been proven feasible, and the company is on track to hit RMB 500 million in revenue this year.

3. The AI memory track has shifted from technology competition to commercialization competition. The future of AI memory lies in systems capable of reflection, forgetting and autonomous evolution, rather than simply storing more information. For the B2B segment, closed-source models are more likely to generate profits.

This shared experience of implementing AI memory provides actionable insights for brands looking to upgrade their marketing and customer service. Key takeaways are as follows:

1. Human-like memory engines solve the core pain points of brands' existing AI customer service. With its "memory first, inference second" logic, the system pulls users' purchase records, device information and historical consultation in advance to deliver accurate responses without repeated questioning. This greatly improves user experience, cuts token communication costs, and reduces brand damage caused by incorrect AI responses.

2. The built-in reflective iteration capability of memory engines can adapt to the personalized customer service needs of different brands, automatically adjusting prompts, question-and-answer annotations and service processes to meet brands' changing operational requirements.

3. The AI memory track is still in an early stage of development, and a consumer-facing breakout hit is expected in the future. Brands can get a head start by building AI-native intelligent customer service and marketing systems to capture the user experience gains brought by technological upgrades and build competitive advantages early.

The commercialization of AI memory technology opens up new opportunities for D2C sellers to upgrade their operations and services. Key takeaways are as follows:

1. In terms of opportunities, AI customer service and marketing systems powered by memory engines solve the common problems of repeated questioning and high error rates of traditional AI customer service. They greatly improve user consultation experience, cut labor costs for customer service, and boost consultation conversion rates. Mature, deployable solutions already exist, so this is not just a conceptual technology.

2. For implementation, sellers can introduce AI memory systems starting from revenue-close scenarios like customer service and marketing first, then use revenue gains from the technology upgrade to fuel digital transformation across more business links, reducing the risk of transformation investment.

3. Risk warning: AI memory inherently processes large volumes of user privacy data, so compliance is the core risk. When selecting service providers, sellers should prioritize checking the compliance of the provider's underlying architecture. Meanwhile, traditional non-AI-native customer service solutions are being displaced rapidly, so sellers need to upgrade in time to retain efficiency advantages.

The development of the AI memory track offers important insights for factories advancing digitalization and e-commerce transformation. Key takeaways are as follows:

1. On the business side, for factories building their own user operation and after-sales systems, memory-driven AI can fully record users' purchase information, product problem feedback and historical interaction data, helping factories accurately aggregate user reports of product issues and improvement demands. This provides first-hand authentic user data to support product design and production optimization, helping factories launch products that better match market demand.

2. Insights for digital transformation: Factories do not need to invest heavily in building a pure underlying digital technology system from the start. They can follow Red Bear AI's path: start with high-impact, easy-to-implement scenarios such as customer service and after-sales, use revenue generated from these business implementations to fuel the build-out of underlying digital capabilities, iterate step by step, and reduce the capital and trial-and-error risks of transformation.

3. In terms of technological change, AI-native applications are replacing traditional digital solutions. When building new digital systems, factories that prioritize AI-native solutions can avoid being quickly outdated by technological iterations.

The current development status, pain points and future direction of the AI memory track are now clear, providing valuable reference for AI-related service providers. Key takeaways are as follows:

1. Industry development trend: The AI memory track was ignited by Gemini and DeepSeek at the end of 2025, and will enter its formal development stage in 2026. The current market size is around RMB 1-2 billion, and has yet to produce a C-end breakout hit, leaving large room for future growth. The industry has shifted from early competition over technological concepts to competition over commercial implementation.

2. Core customer pain points: Most existing memory solutions operate as patches for large models, adding memory after inference, which leads to low accuracy in vertical scenarios and frequent factual hallucinations, failing to meet B-end clients' requirements for business stability. What customers actually need are deliverable, revenue-generating solutions that meet compliance requirements.

3. Technology and commercialization direction: Technically, human-like memory engines are the converging direction of the industry, and require capabilities for reflection, forgetting and autonomous evolution. For commercialization, closed-source models are more profitable in the B2B space, while open-source solutions mostly generate buzz but no tangible revenue. Acquiring real user needs through project-based work to fuel product iteration is a proven feasible path.

The development of the AI memory track offers important insights for platforms looking to build AI capabilities and improve merchant services. Key takeaways are as follows:

1. Merchants' core demand for AI memory capability is deployable, stable performance and compliance. Pure underlying memory infrastructure is not yet ready for large-scale commercialization. Platforms can follow the path of proven industry players: start with high-frequency, high-demand merchant scenarios such as customer service and marketing, integrate mature AI memory capabilities to serve merchants, validate demand and generate revenue quickly, then use that to fuel the build-out of underlying capabilities.

2. Core risk warning: AI memory inherently processes large volumes of user privacy data, so compliance is the core factor determining a project's success. Platforms building related capabilities must meet compliance requirements at the underlying technical architecture level to proactively mitigate data privacy risks.

3. Industry competition trends: The industry has shifted from technology competition to commercialization competition, and traditional non-AI-native solutions are being displaced rapidly. Platforms need to accelerate the build-out of AI-native capabilities to keep up with industry changes. Meanwhile, memory AI will evolve toward autonomous reflection in the future, so platforms need to reserve sufficient space for iteration.

This interview discloses the latest industry trends, technical routes and business models of the AI memory track, providing valuable first-hand reference for industry research. Key takeaways are as follows:

1. New industry trends: The AI memory concept emerged as early as 2023 but failed to gain market traction in 2024. The track was ignited when Gemini and DeepSeek publicly entered the memory technology space at the end of 2025, and will enter a formal growth phase in 2026. The current overall market size of the track is around RMB 1-2 billion, and it still lacks a breakout product that delivers tangible improved experiences for end consumers. Industry competition has shifted from competition over technical routes to competition over commercial implementation.

2. Divergence in technical routes: The track is currently dominated by three technical routes: built-in memory in base models, third-party plugin/patch memory, and human-like memory engines. The third route adopts the "retrieve memory first, inference second" logic, delivering higher accuracy and more effectively reducing factual hallucinations than the first two approaches.

3. Business model innovation: Unlike the industry's common approach of building underlying technology first then developing applications, Red Bear AI has created a new path: monetizing applications first to generate revenue and collect real user demands, then using those resources to build out underlying technology. The company currently operates a hybrid model of project-based services, subscriptions and private deployment, and is on track to hit RMB 500 million in revenue this year, proving the model's feasibility. It also finds that for the B2B AI memory track, closed-source models are more suited for profitability than open-source alternatives.