Analyze ESG Data
Experience the power of knowledge graphs and domain specific search
AI Powered ESG Compliance
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Extracting ESG data from Text documents
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ESG strategy and sustainable investment.
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To recommence with ESG in financial analysis, we will focus on how ESG will play a role in future innovations...
ESG factors in financial analysis
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ESG reporting framework
Environmental, social, and governance (ESG) reporting has become an essential aspect of corporate transparency and accountability...
Stay on Top with ESG
Analyze public data (news, social, etc.) for an outside-in-view of risk and opportunities
Improve Portfolio Performance
Manage Portfolio Risk
Spot Opportunities for Investment
Compliance
Real time Alerts and ESG Breaches
Corporate ESG goals for emission reductions require monitoring breaches of preset thresholds and alerting on such events. For example, Air Quality monitoring to comply with local EPA regulations is supported by TextDistil-ESG through the ‘Alerts’ feature. The measurement data is collected and as the monitored gases in the production plants cross ear marked thresholds alerts are generated and notifications are sent out.
Similarly many other parameters that are collected are monitored as required and configured in TextDistil-ESG for alerts.
ESG Search and Query
Risk & Opportunity Assessments – An exploratory endeavor – With a flexible powerful Search and Query
Handling vast amounts of unstructured data is at the heart of managing ESG programs. TextDistil comes out of the box with a powerful Natural Language Search built on both the more powerful Knowledge graph or Graph RAG as well as the more popular RAG search.
TextDistil search also provides a instant provenance to the granular source data, such as when an analytical query (a form of search) show levels of CO as 8 ppm in a day, the interface provides features to track down the parts from various collected reports that make up this average.
ESG BI and Decision Analytics
The goals of ESG programs are reducing carbon emissions, equitable social environment, and a fair, ethical and responsible governance.
Measuring progress towards ESG goals requires a complex system of tracking, collecting, aggregating, and analysing vast amounts of unstructured data – Regulations, Guidelines, expert reviews, industry reports, operational data streams, news, social media, etc.
Based on the specific industry, thousands of concepts and terms such as ‘renewable power’, ‘carbon intensity’, ‘sustainable aviation fuel’, ‘decarbonization’, etc. must be tracked along with their measures. These measures are reported in various metrics and units such as: ‘giga watts’, ‘tonnes’, ‘barrells’, variety of recorded measures of volume, length, weight, area, temperature, etc.
TextDistil-ESG is configured with an ESG Ontology and Taxonomy, and capable of extracting ALL the ESG entities and their reported measures from the unstructured data (Text, Audio, Video and Images) and make it available in a ESG Knowledge Graph. This Knowledge Graph drives powerful Business Intelligence systems that support complex reporting required by the current ESG regulatory regimes in North America, Europe and Australia. Complex reporting such as the ‘Air Quality monitoring submissions’ in the US are supported.
Unstructured Data Volumes
Current Search Methods
Exploding volumes of ESG text are a challenge to current search and analysis methods.
Automatically created Knowledge graphs from Text are source of valuable insights and integrate seamlessly into enterprise BI and Decision support applications.
Knowledge Graph created from ESG text
ESG Ontology and Taxonomy drive the extraction of domain specific facts from the text. These facts are loaded into the graph database to create the Knowledge Graph.
Knowledge Graph supports Search interface, Query API and Alert interface.
Sample Use Cases
Private Equity
Private equity analyst in analyzing ESG data (a corpus of articles, documents, submissions, etc.) encounters opportunity signals asking about companies’ capital deployments in the space
Portfolio Management
A Portfolio Manager asking about climate claims discovers important ESG signals (in a corpus of articles, documents, submissions, etc.) that might help in developing insights into rebalancing a portfolio.
TextDistil – Robust Cognition Pipeline powering our solutions
TextDistil is built using state-of-the-art large language models and semantic technology
Our ESG Ontology supports custom models which are trained on large Climate and Finance terminologies and taxonomies.
