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Maybe someday you’ll be able to ask ChatGPT to “Please evaluate this hedge fund for me?” and you’ll get a response you can trust—one that’s actually tailored to your firm’s unique evaluation process and investment priorities.
We’re not there yet.
In the meantime, what you definitely shouldn’t do is hand the keys to the data room over to your teenager, have them upload a pile of sensitive fund documents into ChatGPT, and see if they can “figure it out.”
Institutional investors conducting due diligence on hedge funds spend weeks analyzing qualitative and quantitative data across a range of documents—pitch decks, fact sheets, DDQs, etc. This manual, labor-intensive process involves extracting key insights and information and structuring it into formal evaluation reports for investment committee review and approval.
Pure Math AI developed a pilot of an AI-powered system to automate qualitative information extraction from fund documentation and generate structured draft reports. Unlike generic AI solutions, this system was tailored to a specific company's output format, leveraging Pure Math AI’s deep industry expertise to customize prompts for the firm’s proprietary evaluation process and investment priorities.
The Problem: A Slow and Costly Due Diligence Process
Institutional allocators and investment consultants face a time-intensive and resource-heavy due diligence process. Evaluating a private fund typically requires:
Manually reviewing hundreds of pages of fund documentation.
Extracting key qualitative insights from disparate sources (pitch decks, fact sheets, DDQs).
Compiling findings into a structured investment report for approval committees.
Due diligence is not a standardized process—different investors focus on different aspects of manager selection:
Pensions and Endowments prioritize governance, liquidity, and long-term performance consistency.
Family Offices focus on alignment of interests, manager incentives, and tax efficiency.
Fund of Funds and Consultants emphasize risk factor exposures, strategy diversification, and operational robustness.
Managed Account Platforms (MAPs) and Alternative Investment Platforms evaluate hedge funds at scale, ensuring platform compatibility, operational integrity, and institutional-grade transparency before offering access to investors.
Given these variations, an off-the-shelf AI model would be ineffective. Due diligence requires tailored intelligence, not generic automation.
The Solution: AI-Powered Report Generation with Custom Prompt Engineering
To address this inefficiency, Pure Math AI built a custom AI-driven document processing pipeline, designed to:
Scan and extract qualitative insights from fund marketing materials (pitch decks, fact sheets, DDQs) in minutes.
Generate structured due diligence reports that align with institutional investment committee standards.
Custom-engineered AI prompts focused on specific investor priorities that reflect a firm’s unique due diligence process.
Why Custom Prompt Engineering is the Key Differentiator
Pure Math AI’s deep experience in alternative investments allowed it to create custom prompts that aligned with how our client evaluates hedge funds. Instead of applying a one-size-fits-all approach, the extraction criteria can be based on the specific investment philosophy, risk preferences, and governance standards of the investor.
For example:
A pension fund evaluating a hedge fund would receive a report that emphasized governance, liquidity terms, and operational risk oversight.
A quant-focused allocator would get a detailed breakdown of the manager’s investment approach, including execution methodology and systematic strategy structuring.
A family office might focus on alignment of interests, side letter terms, and potential conflicts of interest, rather than broad institutional governance metrics.
The ability to engineer prompts that reflect the nuances of institutional due diligence is what sets Pure Math AI apart from generic AI tools.
Unlike generalist AI models, which return surface-level insights or require heavy post-processing, Pure Math AI’s system extracts structured, relevant insights tailored to the investor’s priorities—right from the start.
Technical Implementation: How We Built It
The system is built on a multi-stage retrieval-augmented generation (RAG) framework, optimizing for both accuracy and efficiency in extracting relevant information. The workflow consists of:
1. Document Ingestion & Preprocessing
Files are parsed from various formats (PDF, PowerPoint, Excel, Word) using OCR-enhanced text extraction for non-machine-readable documents.
Tokenization and sentence segmentation are performed to normalize text across different document structures.
Named entity recognition (NER) and domain-specific regex parsers preprocess key terms, ensuring consistent identification of fund attributes (e.g., AUM, fee structures, key personnel).
2. Context-Aware Information Retrieval
A vector search index maps extracted content to a predefined fund due diligence schema, allowing for semantic retrieval of relevant information.
Custom AI prompts adjust dynamically based on investor-specific evaluation frameworks, ensuring the right insights are prioritized for each firm.
LLM prompt engineering optimizations are implemented to extract targeted insights based on fine-tuned few-shot learning examples.
To reduce hallucinations, we constrain model outputs via contextual anchoring, ensuring responses strictly derive from extracted fund documents.
3. Structured Report Generation
Extracted qualitative data is programmatically mapped to a templated report structure, aligning with institutional due diligence formats.
An LLM-powered summarization layer refines verbose fund descriptions, ensuring conciseness and clarity.
Section-by-section validation is performed via heuristics and rule-based consistency checks, reducing incorrect or incomplete data extraction.
4. Human-in-the-Loop (HITL) Review Integration
The system generates confidence scores for each extracted field, flagging low-confidence extractions for analyst review.
A feedback loop enables incremental model fine-tuning, allowing the LLM to improve accuracy on fund-specific language over time.
Tech Stack & Deployment
LLM Backbone: GPT-4 (Fine-tuning planned for future iterations)
Vector Search: GPT embeddings for similarity-based retrieval
Data Processing: Python PDF packages (such as pdfplumber), Pandas, spaCy (NER)
Orchestration: Python Multi-threading for high-volume OpenAI API usage
Deployment: Containerized and deployed on GCP for scalable processing
The Results: Faster, Smarter Fund Evaluation
By leveraging AI, Pure Math AI’s system enabled:
A 95 percent reduction in time spent drafting due diligence reports.
Higher accuracy by eliminating manual data extraction errors.
A customized due diligence report tailored to the proprietary evaluation process of the client.
For firms conducting frequent hedge fund evaluations, this approach can eliminate bottlenecks in due diligence, freeing analysts and other personnel to focus on analysis, decision-making, or even spending quality time with their family—instead of spending weeks manually pouring over documents, copying and pasting information, and formatting reports.
Contact us to learn more about our AI-powered due diligence solution?
Pure Math Editorial is an all-purpose virtual writer we created to document and showcase the various ways we are leveraging generative AI within our organization and with our clients. Designed specifically for case studies, thought leadership articles, white papers, blog content, industry reports, and investor communications, it is prompted to ensure clear, compelling, and structured writing that highlights the impact of AI across different projects and industries.