At NetMind, we’re driven by the vision that AI should help you with real work.
Recently, we partnered with a leading fintech company to achieve exactly that: helping them transform millions of unstructured financial documents into structured, searchable, and compliant data assets using our Document Intelligence and Retrieval-Augmented Generation (RAG) solution.
Before working with NetMind, the client’s information was scattered across PDFs, which included regulatory filings, investor relations pages, and figure illustrations. Their teams spent hours searching and verifying data manually, a process prone to human error and impossible to scale.
To make things even more complex, finance is a highly regulated environment where sensitive data can't be processed by external services. The company needed an in-house solution that could extract structured content from financial documents while staying fully compliant.
Powered by the latest version of our NetMind Parse Pro MCP tool, our team designed a Document Intelligence platform for our client. Using advanced vision-language recognition and LLMs, our system read and converted complex PDFs into human-readable JSON and Markdown files. Tables, headers, and numerical layouts were reconstructed with high fidelity, creating a foundation for accurate data extraction.
How does it work?
When NetMind Parse Pro detects visual content, it automatically routes the data to an OCR (Optical Character Recognition) system for interpretation. OCR enables machines to “read” text embedded in scanned documents or images, and our OCR system goes beyond simply identifying printed characters. It understands contextual relationships within tables and charts, providing a reliable foundation for downstream AI reasoning.
Building on this clean, structured foundation, our AI agents automatically extract key fields from documents like bank statements and proxy filings. To maintain accuracy across large, complex files, the system also splits lengthy documents into manageable sections and processes them iteratively when necessary, ensuring every data point is captured without loss or duplication.
We layered a RAG-based question-answering system that allows analysts to query across thousands of documents. The system retrieved relevant snippets, passed them to an LLM, and generates grounded answers with full citation to the source documents.
What is RAG?
RAG, or Retrieval-Augmented Generation, is an AI framework that combines the power of large language models (LLMs) with a search or retrieval component. Instead of relying solely on what the model "remembers," RAG retrieves relevant documents or data snippets from a trusted source before generating an answer. This makes responses both more accurate and verifiable, and it's a crucial advantage in regulated industries like finance.
Within seconds, users can ask: “What is the total deposit balance for Q3 across all clients?" and receive a verified, referenced response.
For larger-scale data, we also experiment with a knowledge graph built on 13,000+ financial and market documents, enabling more advanced relationship queries and consistency checks.
What is a Knowledge Graph?
A knowledge graph is a structured representation of information that connects entities through their relationships. In this case of finance, the entities can be companies, transactions, and market events. Instead of storing data in isolated tables, a knowledge graph creates a network of meaning. This allows AI systems to reason over connections (for example, linking a client to its subsidiaries or tracing relationships between filings and market actions), enabling deeper insight and context-aware analytics.
The transformation was immediate. Document processing speed increased by three to five times, while manual workload dropped by nearly half.
Complex tables are reconstructed with over 95% completeness, and end-to-end automation can cover more than 80% of routine document types.
RAG-based queries returned in under three seconds, all within the company’s controlled environment.
Just as importantly, the entire pipeline was designed with data sovereignty and compliance in mind, from ingestion and labeling to inference and storage. Every component can run fully on-premises or in a dedicated private cloud, ensuring that sensitive data never leaves the client’s boundaries.
Behind this success is NetMind V2, our latest model built for enterprise-grade document understanding.
In public and internal benchmarks, NetMind V2 outperformed strong baselines such as MistralOCR, achieving an overall score of 80.2 ± 1.0 on the olmOCR benchmark and 65.9 ± 1.5 on our internal DocParse benchmark, which focuses on financial documents.
These results confirm our solution's strength in document reading, the foundational capability needed for financial intelligence.
As one of our clients put it:
“NetMind delivers the speed and accuracy we rely on for large-scale, real-time document intelligence.” — Will Sun, CTO, Orbit
This project demonstrates how AI, when designed for real-world practice, can unlock actual hidden value and boost efficiency at an unprecedented scale.
Book a demo with our SVP Stacie Chan to turn our AI technology into your AI solutions.