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Law Firm Structured Judgment Element Fact Annotation and Word Report Outsourcing - Monthly Income of 5000

Workflow: Receive pending judgment web files daily through partner law firms or outsourcing platforms. First, invoke open-source p

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Key Fields

FIELD STAMPS

IndustryAI / LLM

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

Receive pending judgment web files daily through partner law firms or outsourcing platforms. First, invoke open-source parsing scripts to automatically extract core structured elements such as party information, basis of claim, essential facts, and burden of proof; then, input into large language models to summarize disputes, generate defense predictions, and reference points for similar cases; finally, output a structured report in Word format and a color-annotated version of the judgment, usable after human lawyer review, and deliver to clients per piece.

🛠 Setup Requirements

Basic Python skills are required to deploy the GitHub open-source judgment report generation project, configure the LLM API interface for natural language summarization, and customize parsing rules and standard Word report templates adapted to different court document structures. The overall setup cycle is about 5-7 days; no complex servers are needed, and regular office computers can run it.

🧰 Toolchain

🔧 GitHub Open-Source Judgment Report Generator
🔧 Claude LLM API
🔧 Python Runtime Environment
🔧 Word Document Editing Tools

💰 Revenue

① Structured reports and annotated judgments charged per piece by small and medium-sized law firms (main revenue): Law firms pay per piece, 200-300 RMB/piece, with monthly order volumes not publicly disclosed; internal reports show stable monthly income of over 5000 RMB, which can exceed 8000 RMB in peak seasons, accounting for about 100% of monthly income (self-reported by merchants, not independently verified); ② Law firm batch similar case retrieval monthly subscription: Single clients pay over 2000 RMB/month, number of signed clients unknown, proportion not split (self-reported by merchants, not independently verified); ③ Usage-based API document annotation (pay-per-use): Charged to law firms based on processing volume; source-loaded legal side-business cases process 2-3 copies per workday, 200 RMB × 20 days = 4000 RMB/month (case statement, not independently verified); internal reports mention marginal cost per batch single piece can be as low as under 5 RMB, contribution amount unspecified; ④ Opportunity item - Licensing structured judgment annotation datasets to legal AI vendors by database, with neither licensing scale nor proportion provided.

💸 Cost

LLM expenses fluctuating based on order volume, approximately 100-200 RMB per month. Office software and open-source tools are free with no extra subscription costs. For batch orders, the marginal cost per piece can be as low as under 5 RMB.

⏱ Time Investment

Invest 2-3 hours daily processing documents, taking on batch orders on weekends, and temporarily increasing working hours during peak periods.

🚀 Getting Started

The first step for beginners is to download and run the krionwu23 open-source judgment report generation project on GitHub, testing parsing accuracy and template adaptability with 10+ public judgments; subsequently, publish service information in legal communities, outsourcing platforms, and law firm cooperation groups, starting with low-priced orders under 200 RMB per piece to practice, accumulating word-of-mouth from 3-5 clients before raising prices to take on batch business.

🔑 Keys to Success

✅ Master legal core concepts such as the basis of claim, essential facts, and burden of proof to ensure summarized content meets the usage standards of legal practitioners
✅ Design standardized Word report templates and annotation rules so commissioned lawyers can complete reviews within 5 minutes
✅ Strictly de-identify the original text of judgments, obscuring sensitive party information to avoid leakage risks
✅ Customize parsing rules for the web structures of judgments from different courts to improve the accuracy and efficiency of batch processing
✅ Familiarize with legal communication logic, attaching core point summaries upon delivery to reduce client review costs

⚠️ 风险

⚠️ If errors exist in the annotated legal points, they may mislead lawyers in handling cases, leading to customer complaints or even liability for compensation
⚠️ Web structures of judgment documents across different courts vary significantly, which may cause parsing failures requiring manual secondary processing and affecting delivery efficiency
⚠️ Some judgments involve commercial secrets or personal privacy, and improper handling may lead to compliance risks
⚠️ Over-reliance on a single open-source tool could cause business interruption if the tool stops maintenance or API interfaces are adjusted

📌 Real Cases

📌 DuckDB Lab Public Case: Individual practitioners using large models like Claude for legal document side businesses, achieving a stable monthly income of over 5000 RMB
📌 A Shenzhen LegalTech Outsourcing Team: Adopting a similar judgment structured annotation workflow, undertaking batch similar case retrieval business for 20 small and medium-sized law firms, with stable monthly revenue exceeding 30,000 RMB
📌 Actual Application of GitHub Open-Source Project krionwu23/legal-case-report: Over 200 legal workers have used this tool to improve similar case retrieval efficiency, with some individual service providers charging 150-400 RMB per piece and monthly order volumes reaching 30+ pieces

Sources