Frequently Asked Questions

Cognaize offers a unique document automation solution combining best-in-class AI models with process innovation, leading to superior results. Our AI models are trained on millions of financial documents and are combined with an intuitive UI for financial experts.

Our AI models boast an unprecedented accuracy due to our vertical specialization and downsized models focused on financial services use cases.

A 67% reduction in cost and cycle time has been achieved in a leading financial services company.

Global banks, rating agencies, and other financial service firms in the US, Canada, Europe, and APAC leverage our AI solutions for financial spreading.

Cognaize transforms unstructured data into decision-ready information using hybrid intelligence, improving data quality with each processed document.

It is designed exclusively for financial organizations, putting experts at the center of automation and ensuring high-quality outcomes while maintaining strict control over intellectual property, auditability, and data quality.

Cognaize offers a unique document automation solution combining best-in-class models with process innovation, leading to superior results. Their deep learning models are trained on millions of financial documents integrated into an intuitive UI for financial experts.

Cognaize specializes in AI-based document automation for financial services. Sub-sectors include lending (consumer, mortgage, small business, commercial), insurance (auto claims, home insurance claims, mailroom automation, ESG), and investment banking (trade execution, investment monitoring, analysis, ESG). However, one of our advantages is a high degree of flexibility when it comes to processing and annotating documents.

Financials, bank statements, pay stubs, tax returns, rental agreements, valuation reports, board resolutions, settlement instructions, credit loan agreements, sustainability reports, emails, SEC filings, trustee reports, and investment portfolio statements.

We believe in - what we call - AI-adequacy. Our models are exclusively trained on financial data. We are moving from Large Language Models to Precise Language Models tailored for the financial industry. They are smaller, faster, more cost-effective, and less resource-intensive. Our models offer enhanced accuracy, fewer hallucinations, and are suited to run on the client infrastructure, ensuring better privacy. Our enhanced layout understanding means that our models incorporate layout details of documents, allowing them to understand and extract information based on tables, font styles, and word placement, leading to more accurate data interpretation in complex financial documents.

The concentration of AI deployment capabilities among a few large tech companies due to the high computational demands of large language models, posing risks to innovation and society at large.

The process of making AI training and capabilities accessible to a broader range of users and applications, enabling more versatility and adaptability in AI systems.

Analyzing documents and data using AI to derive insights and aid decision-making.

The use of AI to systematize workflows, personalize customer experiences, and create novel products and services, requiring an understanding of AI's capabilities and ethical standards.

The use of AI to automatically extract data from documents with high accuracy.

AI models with fewer parameters developed by companies like Cognaize to exhibit social learning skills for specific tasks, reducing the risks of centralization and hallucination in AI applications. In our case with a vertical specialization in financial services.

Advanced AI technologies used for processing and understanding complex documents.

AI models specifically trained on all kinds of documents used in the financial sector for accurate data extraction, annotation, and analysis.

Combines AI technologies and human expertise for intelligent document processing.

AI systems, like GPT-3 and ChatGPT 4.0 that understand and generate natural language characterized by a massive number of parameters, requiring significant computational resources.

A method to finetune a pre-trained model, like GPT-3, into a quantized version without significant loss of accuracy, by adjusting parameters for low-bit quantization.

A framework of neuron-like units in AI systems, where connections between units are adjusted through algorithms like backpropagation to minimize errors during tasks.

A technique to convert high-precision floating-point numbers in AI models to low-precision integers, reducing computational and memory requirements, crucial for deploying large-scale models like LLMs.

A finetuning method that simulates the effects of quantization during training, making the model robust to quantization effects and maintaining accuracy in a quantized deployment.

The capability of AI systems to learn new tasks through instruction prompts without modifying their underlying weights, mimicking a form of learning previously thought exclusive to natural intelligence.