Financial Document
Automation with

Award-Winning AI Agents

For example, a bank might have its own internal definition of Cost of Goods Sold (COGS) that differs slightly from the standard accounting definition. A verifier ensures that AI models working with the bank's financial data adhere to this specific definition.

Neural networks excel at pattern recognition and handling unstructured data. Symbolic AI is adept at logical reasoning and working with structured knowledge.

By integrating these approaches, neuro-symbolic systems can process complex, real-world data while also incorporating explicit rules and financial domain knowledge.

This approach not only enhances text extraction but also comprehensively captures visual elements, such as charts and infographics, previously overlooked by traditional systems. With fine-tuned models like the PaLM-Gemma-3B and optimized techniques including quantization, Cognaize offers an exceptionally efficient solution, outperforming other models while minimizing computational demands.

For financial institutions, this means unlocking valuable insights faster and more accurately from a wide range of complex documents, enabling smarter decision-making and streamlined operations.

Addressing this challenge, Cognaize employs a "Mixture of Experts" (MoE) architecture, integrating specialized models—including downscaled and hybrid machine learning system–to optimize processing by selectively activating models as needed.

With Compound AI, the system can process inputs with multiple models simultaneously and apply explicit rules at critical stages for verification, reducing errors and reliance on human intervention. This approach not only enhances accuracy but also continuously refines the model’s effectiveness over time by learning from new data inputs, ensuring cost-effective scalability and precision.

Our platform is built to support GDPR and other regulations seamlessly.