GaiaLensCo-Founder
Oct. 2020London, England, United KingdomAs CEO of GaiaLens, one of my main responsibilities is as the Product Owner. Through this experience, I lead a tech team of 15 people including data scientists, data engineers and JavaScript developers. I built the ESG Analytics prototype using Python, SQL and PowerBI as a frontend, enabling us to raise our seed round. I manage and oversee our tech stack which includes Google Cloud Platform (GCP) as a cloud provider, Postgres database (running on Cloud SQL), Python to run our models each day (running on VMs), React.js (with TypeScript) front-end. Through building an ESG data product from scratch and immersing myself in the space, I have gained expertise in ESG/sustainability data and analytics and building scalable financial software solutions. We use the latest technologies across our product. This includes implementing AI throughout our product to extract data from company reports (which are PDFs), predicting forward-looking ESG scores for companies, engineering proprietary factors and using LLMs to process the news at scale: 1. PDF Extraction: we use a combination of embeddings and LLMs to efficiently search a PDF for the information we are looking for e.g. a company’s carbon emissions and extract that data.
2. Forward-looking ESG Scores: we calculate real-time, customisable, and transparent ESG scores. We use a hybrid approach, blending time series and machine learning to compute forward-looking scores. We calculate ESG scores for over 20,000 listed companies and have historical scores going back 20 years.
3. Proprietary ESG Factors: The GaiaLens system uses a combination of intelligent web crawlers and Machine Learning to engineer innovative proprietary features such as diversity metrics at scale and in real-time.
4. Real-time ESG News: we have millions of articles that we generate signals from using the latest Large Language Model (LLM) technologies.