成立12年的一家法律科技公司，为什么在大模型时代能获得资本关注？

This article introduces the development journey and key information of Zhihe, a legal tech firm with 12 years of industry focus that recently secured tens of millions of RMB in Series A funding amid the generative AI boom. Key takeaways are as follows:

1. Core background: Founded in 2014 by Hong Zuyun, a seasoned legal practitioner with nearly a decade of industry experience, Zhihe was launched to address the supply-demand mismatch in China’s legal services market. Current industry data shows 98% of Chinese enterprises do not retain full-time in-house legal counsel, and Supreme People’s Court data indicates 78% of individual litigants in first-instance court cases proceed without legal representation. A large number of small and medium-sized entities lack access to affordable, ongoing legal services.

2. Development path and current status: Zhihe did not rush to develop AI in its early stages. Instead, it started with legal media, industry events and lawyer training to build its user base, map out use cases, and accumulate industry expertise. It only shifted to AI R&D after large language models became widely available. Currently, Zhihe’s AI product has nearly 3,000 daily active users, with 120,000 to 150,000 usage sessions per day on average.

3. Key takeaway: For vertical sector startups in the generative AI era, deep industry expertise and accumulated domain knowledge create stronger long-term competitive moats than chasing cutting-edge technical specifications alone.

This article offers actionable insights for brands looking to enter the legal tech space, as outlined below:

1. Consumer trend and demand insight: China’s professional legal services market faces a clear supply-demand mismatch. A large volume of small and medium-sized enterprises (SMEs) and individual users have recurring legal needs but cannot afford the cost of traditional high-end legal services. There is a huge unmet market gap for affordable, accessible routine legal services targeting small and medium-sized entities, which brands can target as a large long-tail market when expanding into this sector.

2. Product development reference: Instead of blindly chasing technology hype, Zhihe prioritized deep industry immersion to map user needs and build domain expertise before rolling out AI-powered products. This approach avoids the pitfall of building technology disconnected from real market needs, making it a more reliable path for brands developing new products.

3. Building long-term brand value: As generative AI technology becomes increasingly commoditized, a brand’s true competitive moat lies in its long-built user base and deep understanding of industry use cases. This long-term value is widely recognized by both investors and the market, and serves as a valuable lesson for brands.

This article provides key insights for sellers operating in legal services or AI-related sectors, covering market opportunities, actionable experience and risk warnings:

1. Market opportunity and shifting demand: There is a massive unmet gap in China’s legal services market: 98% of SMEs do not retain permanent legal counsel, and a large share of individual users also lack access to routine legal services. Large language models have lowered the barrier to scaling legal capabilities, making lightweight, low-cost legal services for small and medium-sized entities a new growth market that aligns with SMEs’ growing demand for operational risk management.

2. Actionable development lessons: Avoid blindly chasing hype. First build industry depth, user base and domain resources before integrating new technology to build sustainable competitive advantage. Sellers can adopt Zhihe’s "AI agent + lawyer" hybrid service model, which combines AI’s low-cost advantage with human lawyers’ professional expertise to effectively serve long-tail demand.

3. Risk warning: Large generalist model providers will most likely enter the vertical legal sector in the future. Pure technology-focused players will struggle to build defensible moats. Sellers need to capitalize on the current window of opportunity to build industry expertise and user base as early as possible to form their competitive moat.

This article offers key insights for manufacturing factories, focusing on digital transformation lessons and business opportunities:

1. Digital transformation takeaway: For factories pursuing digital transformation in the generative AI era, there is no need to blindly chase the most cutting-edge technical specifications. The core priority is to align AI deployment with actual production and management workflows, accumulate in-house business expertise first, and then integrate large model technology for upgrades. Zhihe’s path of building domain expertise before AI development avoids the common pitfall of digital projects disconnecting from actual business needs, making it a highly valuable reference.

2. Operational business opportunity: The vast majority of Chinese SMEs do not retain permanent legal counsel, and factories face a large volume of unmet legal needs across contract signing, compliance management and other core operational processes. Factories can leverage large model-powered legal AI tools to access affordable ongoing legal support and effectively reduce operational risk.

3. New business expansion direction: For service providers that serve small and medium-sized manufacturers, integrating mature legal AI capabilities allows them to expand their service scope and unlock new growth opportunities.

This article shares key insights for professional service providers, covering industry trends, client pain points and go-to-market strategies:

1. Core industry trend: In the generative AI era, general model capabilities are increasingly commoditized into baseline infrastructure. The core competitiveness of vertical sector service providers is no longer technical capability, but rather their understanding of industry use cases and ability to embed AI into real-world workflows. Vertical service providers with long-term domain accumulation are increasingly favored by investors, which defines the core development trend of the current vertical AI services industry.

2. Key client pain points: The core pain point in the legal services sector is the supply-demand mismatch. A large number of SMEs and end consumers have recurring legal needs, but traditional legal services are costly and hard to access, leaving a massive market gap that is a core entry opportunity for new service providers.

3. Actionable solution reference: Providers can follow the path of first building user base and domain expertise, then integrating new technology to launch products. Adopting the "AI agent + lawyer" hybrid service model balances scalable low-cost delivery with professional expertise, and this model can be replicated across many professional service sectors.

This article provides key insights for platform operators including AI platforms and legal service platforms:

1. Unpacking market demand: In the current generative AI ecosystem, platform users have strong demand for vertical AI applications tailored to specific use cases. Pure general-purpose large models cannot meet the actual needs of professional sectors like law, and users require vertical AI services that can integrate directly into real workflows. Platforms need to onboard vertical players with deep domain accumulation to enrich their ecosystem.

2. Platform strategy and operational direction: Platforms should capitalize on the current window of opportunity for vertical players to proactively onboard vertical AI projects with long-term industry accumulation. These projects are more likely to deliver on product-market fit and build sustainable moats than pure technology-focused startups. Platforms can also reference Zhihe’s "AI agent + lawyer" model to build cooperative lawyer networks, connect AI capabilities with professional service resources, and serve long-tail user demand.

3. Risk mitigation guidance: Do not put undue faith in technical specifications alone. Large generalist tech firms will inevitably enter vertical sectors in the future. Only projects with deep domain expertise and an established user base can build defensible moats. Platforms should prioritize these types of projects when expanding their ecosystem to reduce cooperation risk.

This article shares key insights for industry researchers focused on new trends, open questions and business model innovation in the vertical AI industry amid the generative AI boom:

1. New industry trends: Generative AI has reshaped the valuation logic for vertical AI companies. Previously, the core value of vertical AI companies lay in their technical capabilities. After large model commoditization, model capabilities have become basic public infrastructure. What is truly scarce now is deep understanding of vertical industry use cases and the ability to embed AI into real workflows. Vertical players with years of industry accumulation have regained favor from capital, which marks a key new shift in industry development in the generative AI era.

2. New research questions: General large model providers will inevitably expand downstream into vertical sectors in the future, and how vertical AI companies can build sustainable moats is a core industry question. This article puts forward the direction of prioritizing deep domain accumulation over technical parameter competition, which opens up new avenues for research.

3. New business model reference: Zhihe’s "AI agent + lawyer" hybrid service model, as well as its positioning as an intelligent infrastructure provider for legal services, differs from both traditional law firms and pure-play AI legal companies. It represents a new business model for generative AI application in professional services that merits further in-depth research.