Nature-Positive is a global societal goal defined as 'Halt and Reverse Nature Loss' by 2030 on a 2020 baseline. Nature positive puts nature at the centre of business decision-making, in the same way as financial returns and human wellbeing. The challenge is that the market known as "nature finance" is in its infancy, not least because there remains no easy way to capture risks and opportunities in relation to nature, unlike carbon emissions for climate. Without improving levels of integrity, transparency, monitoring, reporting, verification and connectivity around nature-based solutions, there will be continued uncertainty that hinders large-scale movement of capital towards desired net-zero and nature-positive investments. Auquan's vision, partnering with University College, London is to solve this significant, unmet, global need by pioneering a novel technology, "RAG AI" (Retrieval Augmented Generation), that overcomes the limitations of generative AI. This project will dramatically extend our technology by leveraging RAG AI to create a comprehensive and accurate nature-positive database, ensuring complete data coverage and low false positives. Pilots with early-adopters will measure and demonstrate benefits.

Information overload is a common problem, particularly in data-heavy industries like professional and financial services, specifically in regulatory compliance, where keeping track of regulatory developments across multiple geographies and asset classes is a major challenge. Unstructured text such as KYC/KYB risks and the associated discussions, contain much valuable information that is often missed by data analysts and key decision makers, for example when assessing risk and implications on business operations. We aim to: 1) Spot all KYC/KYB risks, and 2) personalise the information discovery/insights generation for each user. This is the intelligence enhancement part and is a step up that aims to address the limitations of Generative AI. No solution exists today to solve this problem, this is mostly done in house by a team of analysts who often manually go through all content online. This is often not exhaustive and can be inaccurate. Auquan will solve this significant, unmet, global need by developing technology that exhaustively extracts information from unstructured datasets and enhances the "so-what" of the data to reveal relevant insights without overwhelming the user with high volumes of data, ultimately leading to better decisions and more productive analysts. We also plan to investigate exciting wider use-cases for the technology outside the finance sector including in the insurance and legal sectors.

Information overload is a common problem, particularly in data-heavy industries like professional and financial services, specifically in regulatory compliance, where keeping track of regulatory developments across multiple geographies and asset classes is a major challenge. Unstructured text such as regulatory updates and the associated discussions, contain much valuable information that is often missed by data analysts and key decision makers, for example when assessing risk and implications on business operations. We aim to: 1) Spot all regulatory changes, and 2) Classify the changes under right categories (to be defined) so that they can be routed to the right teams. This is the intelligence enhancement part. No solution exists today to solve this problem, this is mostly done in house by a team of analysts who often manually go through all content online. This is often not exhaustive and can be inaccurate. Auquan will solve this significant, unmet, global need by developing technology that exhaustively extracts information from unstructured datasets and enhances the "so-what" of the data to reveal relevant insights without overwhelming the user with high volumes of data, ultimately leading to better decisions and more productive analysts. We also plan to investigate exciting wider use-cases for our technology outside the finance sector including in the insurance and legal sectors.

Information overload is a common problem, particularly in data heavy industries like finance. Unstructured text, such as company reports, news, transcripts, emails and memos, contain much valuable information that is often missed by data analysts and key decision makers, for example when assessing risk and investments. Access to scientifically robust climate and environmental data and analytics is particularly patchy and unreliable. To make accurate decisions and avoid green-washing, financial firms have to rely on more than just company disclosures about the sustainability of their business activities. However, current solutions do not visually display these links in datasets that could help decision makers make better, more informed decisions. Auquan will solve this significant, unmet, global need by developing knowledge graph technology that visually links relevant, but often hidden, insights without overwhelming the user with high volumes of data. Our solution extracts information from both unstructured and structured datasets, leading to better decisions and more productive users in the finance sector. This is a big step change in 'greening finance', because it ensures that financial risks from climate and environmental factors are seamlessly integrated into mainstream financial decision-making.

Information overload is a common problem, particularly in data heavy industries like finance. Unstructured text, such as company reports, news, transcripts, emails and memos, contain much valuable information that is often missed by data analysts and key decision makers, for example when assessing risk and investments. Current solutions do not visually display the links in datasets that could help decision makers make better, more informed decisions. Auquan is solving a significant, unmet, global need by developing technology that visually links relevant, but often hidden, insights without overwhelming the user with high volumes of data. Our solution extracts information from both unstructured and structured datasets, leading to better decisions and more productive analysts. Automating information retrieval from unstructured datasets applies to many sectors, not just finance. It is particularly challenging because it requires custom extraction (e.g. differentiating between tables/text) from different data formats (e.g. presentations/PDFs/reports) and domain-specific (e.g. legal language versus financial text) training of NLP algorithms. This project aims to dramatically extend and improve our technology, then undertake pilot evaluations with early adopters to measure/demonstrate that our actionable insights save time and deliver better performance. We also plan to investigate exciting wider use-cases for our technology outside the finance sector including scientific/medical literature.