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AI Litigation Discovery Summary Subscription, 3,000 RMB per case, law firm monthly flat fee of 20,000 RMB

Workflow: Receive the complete litigation discovery document package uploaded by partner law firms every morning via encrypted cha

AGENT

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

Receive the complete litigation discovery document package uploaded by partner law firms every morning via encrypted channels, including emails, contracts, chat records, and scanned vouchers. First, call OCR tools to recognize the content of scanned documents, then match similar case processing rules through a vector database. The AI automatically extracts key factual nodes, timelines, dispute foci, corresponding legal provisions, and evidence chain correlations, generating structured summaries with original page numbers and paragraph annotations. After secondary manual verification of legal terminology accuracy, the summaries are sent back to the law firm through encrypted channels, and fees are settled on a per-case basis.

🛠 Setup Requirements

Requires basic Python data processing skills, familiarity with LangChain or Dify for AI workflow orchestration, configuration of LLM APIs such as Tongyi Qianwen or OpenAI, deployment of tools like PaddleOCR to process scanned documents, and setting up a Milvus vector database to store the legal knowledge base and historical case data. The overall setup cycle is 3 to 4 weeks. In the initial stage, open-source litigation document processing projects on GitHub can be directly reused for rapid iteration without training large models from scratch.

🧰 Toolchain

🔧 LangChain
🔧 Dify
🔧 OpenAI API
🔧 Milvus
🔧 PaddleOCR

💰 Revenue

① Litigation discovery summaries settled per case with partner law firms (main revenue): Law firms pay per case, 3,000 RMB/case × 7 to 8 cases per month = monthly revenue of 21,000 to 24,000 RMB. About 87%-100% of monthly revenue is contributed by this channel. This range is calculated backward by multiplying the per-case price by the case volume, based on the case owner's self-statement and has not been independently reviewed; ② High-frequency case type premium: Case types such as labor disputes and financial loans are charged at 5,000 RMB/case. Top performers handling over 15 cases per month claim monthly revenue can exceed 40,000 RMB. The exact proportion of this in total revenue is not provided, and it is likewise only a statement from the case without independent verification; ③ Long-term subscription package: Law firms pay a monthly fixed outsourcing fee for a fixed case volume. There is no public data on the package price or the number of contracted law firms, and its proportion in total revenue is also unclear; ④ Opportunity: Commission on new contract value from law firms: Using a public case's new annual contract value of about 5.8 million RMB as a reference, the commission percentage is not specified, nor has the revenue it generates been verified (stated by the merchant, unindependently reviewed), and the proportion is also unknown.

💸 Cost

LLM API calling cost is about 20 to 100 RMB per case, including content extraction and semantic analysis fees; server and vector database subscriptions are about 50 to 200 RMB per month. The overall cost accounts for less than 5% of revenue, and the profit margin can reach over 90%.

⏱ Time Investment

Processing time per case is about 3 to 4 hours, including document cleaning, AI generation, and manual proofreading, with a daily investment of about 6 to 8 hours. If processing similar standardized cases, the time per case can be compressed to within 2 hours, and unit time revenue can be increased through batch processing.

🚀 Getting Started

Step 1: Download an open-source litigation document processing project from GitHub (such as FachuanHybridSystem), set up a local testing environment, and practice the summary generation workflow using public judgment documents. Step 2: Proactively contact 3 to 5 local small and medium-sized law firms, offering a free trial quota of 2 cases in exchange for real feedback and case endorsement. Step 3: Establish a revenue-sharing cooperation mechanism with practicing lawyers, with lawyers responsible for final legal verification to reduce compliance risks.

🔑 Keys to Success

✅ Clear per-case billing model with high law firm budget approval rates, eliminating long-term subscription sunk costs
✅ Summaries must include original text citations to ensure facts are traceable and verifiable, meeting law firm compliance requirements
✅ Focus on 1-2 types of high-frequency cases (such as labor disputes, contract disputes) to build vertical processing capabilities and improve summary accuracy
✅ Establish a compliant cooperation mechanism with practicing lawyers, who are responsible for final verification to avoid risks of incorrect legal application

⚠️ 风险

⚠️ AI-generated content carries legal accuracy risks. Without secondary review by lawyers, it may lead to litigation strategy errors, and joint and several liability may be borne
⚠️ Litigation documents contain a large amount of sensitive personal information. If the processing pipeline does not comply with the confidentiality requirements of the Personal Information Protection Law and the Lawyers Law, administrative penalties or civil compensation may be faced
⚠️ Some top-tier law firms have self-developed internal AI summarization tools, and market demand may be gradually compressed, requiring continuous iteration of service models

📌 Real Cases

📌 Shenzhen Zhiquan Law Firm optimized its case acquisition process through Luolan Yijing B2B legal AI services. Similar AI summary tools can shorten the litigation discovery phase time by 60% in their internal testing, confirming the real market demand in this track.
📌 Beijing Yaotu Network Technology's legal AI workflow tool gained over 200 registered lawyer users in its first month online, and the automatically generated case summaries achieved an accuracy rate of 92%, verifying the implementation feasibility of the technology.
📌 The GitHub open-source project legal-document-assistant has garnered over 2,000 stars and is used by multiple small and medium-sized law firms for litigation discovery document pre-processing, validating the legal scenario adaptability of open-source tools.

Sources