Frequently Asked Questions
Frequently asked questions about us, the Enterprise Knowledge Platform, Melody, and Hybrid Minds. We've also included a Cognaize glossary for extra credit.
About Cognaize
Cognaize is at the forefront of transforming intelligent document processing in the financial sector through advanced AI. Our foundation is built on the idea that the financial world can benefit greatly from smarter ways to analyze vast amounts of unstructured data. By integrating deep learning and AI, we aim to unlock valuable insights for better decision-making. Our commitment is rooted in using innovation and technology to deepen understanding and enhance decision-making in finance.
Our headquarters is located in the heart of the global financial hub of New York City. We also have offices in Frankfurt, continental Europe’s financial capital, Los Angeles on the US Westcoast, and Armenia, according to Forbes, one of the world’s next tech hubs.
A team of 350 professionals dedicated to blending human financial expertise with AI capabilities. At Cognaize, we promote and celebrate a culture of meritocracy driven by empowering people to always aspire to do their best and support each other in individual and team achievement.
Cognaize believes in the synergy of domain knowledge and AI innovation, focusing on hybrid intelligence. Our philosophy includes understanding clients' problems and goals, challenging the status quo, upholding scientific excellence, making data-driven decisions, and acting with full accountability. We focus on harnessing hybrid intelligence to empower the financial industry and enhance efficiency.
Cognaize is recognized for achievements in AI and hybrid intelligence for transforming decision-making for financial services organizations. In October 2023, CB Insights named Cognaize, its sixth-annual Fintech 100 ranking - showcasing the 100 most promising private fintech companies of 2023.
"Hybrid Minds: Unlocking The Power of AI + IQ" podcast, powered by the minds at Cognaize, explores the groundbreaking concept of combining artificial intelligence with human expertise to achieve unparalleled advancements in various fields. In each episode, we'll bring you fascinating discussions with experts at the forefront of this hybrid intelligence revolution, discovering the transformative impact on various industries, from finance and engineering to education and beyond.
Cognaize was founded by Vahe Andonians, a pioneer in AI and Technology, CTO, CPO, and senior lecturer at the Frankfurt School of Finance. Vahe founded Cognaize to realize a vision of a world in which financial decisions are based on all data, structured and unstructured. Under his guidance, Cognaize has created products that empower banks, insurers, asset managers, and data providers with high-quality data.
Cognaize has completed the SOC 2 examination and is SOC 2 Type 2 compliant, which demonstrates our dedication to maintaining the highest standards of security, availability, processing integrity, confidentiality, and privacy for our client's sensitive information.
SOC 2 is a standard developed by the American Institute of Certified Public Accountants (AICPA) to assess an organization's information security and privacy controls. It serves as the industry benchmark for companies and products that utilize data storage. This compliance affirms that our existing and future clients can be confident that their data is being handled with the proper security, confidentiality, and privacy controls. On a more practical level: customers deploy our downsized and quantized model on-premise or in private clouds where their data never leaves their sphere of influence.
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Enterprise Knowledge Platform
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.
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Other
We cognaized over 800,000 documents YTD using hybrid intelligence.
By integrating AI into the daily operations of business and data science teams.
Please contact us via the contact form or schedule a demo. Our experts will go through your business needs, and all the processes and provide the necessary information to you.
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Cognaize Glossary
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.
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