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Clarum AI Private Equity Due Diligence Agent: Automatically Generate Investment Memos for Monthly Revenue of 80k RMB

Workflow: Receives user-uploaded due diligence target company materials daily, including industry reports, management interview tr

AGENT

Key Fields

FIELD STAMPS

IndustryFintech

RegionGlobal

ScaleSME

ChannelOnline

🔧 Workflow

Receives user-uploaded due diligence target company materials daily, including industry reports, management interview transcriptions, and financial documents. The AI agent automatically extracts key metrics, summarizes risk points, and generates draft investment memos, outputting structured documents for analysts to manually review and revise. The system splits chapters according to industry templates, such as market size, competitive landscape, financial health, and exit paths, with each section accompanied by cited source file page numbers, reducing the time analysts spend searching through raw materials.

🛠 Setup Requirements

Requires a foundation in financial data analysis and prompt engineering skills. Common tools include OpenAI API or Claude API for processing long documents, Python for file parsing and format conversion, and vector databases to store historical due diligence cases. The setup cycle is about two weeks, with costs mainly coming from API usage fees and server rentals. First, establish a set of standard due diligence memo templates, solidifying common chapters and mandatory fields, and then integrate financial data cleaning scripts to process tables in Excel and PDF.

🧰 Toolchain

🔧 OpenAI API
🔧 Python
🔧 Vector Database
🔧 Document Parser

💰 Revenue

① Small and mid-sized private equity funds and PE analyst team (main revenue, billed per copy): priced at 3,000-8,000 RMB per draft investment memo × 15-25 deliveries per month = monthly revenue of 45,000 to 200,000 RMB (converted from unit price and delivery volume). The estimated monthly revenue of about 80,000 RMB falls within this range, accounting for roughly 100% of monthly revenue (this range is a converted value from the case study and has not been independently verified); ② The same group of clients billed on a monthly subscription basis: single client monthly fee of 5,000-15,000 RMB × 10-20 stable clients = monthly revenue of 50,000 to 300,000 RMB (also calculated based on case data, unverified independently). This is an alternative model to ①, and its exact share of total revenue is unspecified; ③ Early-stage hourly or project-based billing: clients who validated the workflow were charged hourly, with neither the hourly rate nor the hours invested disclosed, and its proportion of revenue is unstated; ④ Opportunity item — niche track prompts and no-code workflow package licensing: Clarum claims to be trusted by 2,000+ startups (from company self-description). Licensing reusable templates and prompt sets to analysts in niche sectors like consumer/SaaS has unannounced pricing, and the revenue contribution of this item is also unstated.

💸 Cost

API fees are approximately 2,000-4,000 RMB/month, mainly for GPT-4o or Claude Sonnet invocation costs in processing long documents. Vector databases and server rentals are about 500-800 RMB/month, and document parsing tools like LlamaParse subscription are about 200 RMB/month. Total cost is approximately 2,700-5,000 RMB/month, leaving ample profit margin.

⏱ Time Investment

2-3 hours per day, mainly used for client communication, quality checking AI outputs, and updating the industry knowledge base. Once the agent workflow is established, it can batch process draft copies for multiple projects, with humans only performing final reviews and client delivery, requiring no round-the-clock standby.

🚀 Getting Started

First, select a niche track such as consumer or SaaS, and collect 20 public due diligence cases as training samples. Use no-code platforms like Flowise or Dify to build the first prototype, integrate GPT-4o or Claude 3.5 Sonnet to process long documents, and find 3 PE analyst friends to test and provide feedback. After successfully running a paid case, break down the workflow into reusable templates and prompt sets, and then gradually expand to more industries.

🔑 Keys to Success

✅ Template the output structure so analysts only need to modify numbers rather than rewrite frameworks, reducing their resistance
✅ Continuously accumulate a vertical industry knowledge base, consolidating due diligence conclusions from different projects to improve usability
✅ Manual review is indispensable; the agent only produces drafts to lower professional risk, using source file citations to increase credibility
✅ Cut in by industry niche, mastering one track like consumer or SaaS first before replicating to other domains

⚠️ 风险

⚠️ AI hallucinations may lead to financial data errors, requiring strict manual verification and disclosure of the AI's scope of involvement prior to delivery
⚠️ High compliance requirements in the financial industry mean some clients may reject pure AI outputs or demand additional non-disclosure agreements
⚠️ Open-source models iterate quickly, and clients might build their own internal agents to bypass external services, leading to lost orders

📌 Real Cases

📌 Clarum AI received investment from YC W24, positioning itself as an AI agent for private market due diligence, monitoring, and reporting, serving PE/VC analysts to automatically aggregate due diligence materials and generate draft investment memos.
📌 Clarum's official website demonstrates that its agent can process industry data, interview transcripts, and financial documents, automatically outputting structured due diligence summaries to reduce repetitive labor for analysts.
📌 Reports from Fondo point out that Clarum targets private equity due diligence scenarios, utilizing AI as an analyst assistant rather than a replacement, emphasizing human review and citation traceability.

Sources