The first automated end-to-end ESG workflow

Asset Managers 

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Frequently asked questions

What does Spectrum ESG do for companies?

Weave Spectrum ESG employs advanced AI to automate the intelligence, analysis, benchmarking, and ongoing improvement of a company’s ESG performance.

What specific data points do you provide?

Spectrum ESG automates industry-wide ESG question-answering with the immense power of an intelligent AI assistant. Gap analysis helps you identify specifically where you need to improve relative to a variety of peer groups. Peer analysis enables you to dive deep on a specific top-performing peer to identify why it has high ratings to emulate their best practices.

What about for asset managers and institutional investors?

Spectrum ESG helps investors evaluate the materiality of ESG claims, improve portfolio allocation, reduce investment risks, and better engage clients, regulators, and companies.

What companies do you cover?

We cover all large and mid-cap public companies in the U.S., Europe, and Canada, as well as large cap and most mid-cap companies in other regions around the world.

What if we want to benchmark a public company that isn’t already in your database?

If a company isn’t already in our database, we will include it if it meets our criteria.

How long does it take you to include a new company?

It takes between 24 and 48 hours to include a new company in an existing benchmark.

How long does it take to create a new benchmark?

It takes less than an hour to create a new benchmark.

What are your data sources?

Spectrum ESG analyzes ESG reports, ESG-related documents (such as diversity reports, climate impact reports, etc.), webcast transcripts, earnings call transcripts, annual reports, press releases, regulatory filings (such as 10Qs), and other reports. Spectrum ESG also analyzes news articles from authoritative sources to detect ESG-related risks, controversies, and positive developments.

How often is your data refreshed?

Our ESG data and benchmarks are updated 24/7.

How far back does Spectrum ESG data go?

For most companies ESG data goes back about a decade.

Can I include custom reports in a specific benchmark?

Yes. You can an unlimited number of PDFs to analyze in a specific benchmark.

Who has access to my data?

Only you will have access to your data. Your reports will be encrypted and secured in a private repository on Amazon Web Services (AWS) S3.

Comparing larger companies with smaller ones often leads to misleading results. How do you handle this?

Weave.AI includes sophisticated debiasing algorithms to ensure that larger companies do not end up with inflated gap analysis scores merely because they have more disclosure volumes or dramatically more news coverage. Our AI also considers the size of the company while determining how material a particular investment is.

Your ESG scores are relative to a peer group. How do you determine a company’s peer group?

We determine a company’s peer group using the Global Industry Classification Standard (GICS), a popular industry taxonomy developed by MSCI and Standard and Poor’s (S&P).

Do you support custom peer groups?

Yes. Customers can create custom benchmarks with a specific set of companies they wish to compare themselves against. For instance, some big-cap customers might want to compare themselves not only against their peers but against other big-caps in their region (e.g., the EU). Asset managers might also want to create custom benchmarks for certain companies that straddle multiple industries (e.g., benchmarking Tesla against either automotive peers or solar peers, or Amazon against either technology peers or retail peers).

What is the Weave ESG Knowledge Graph?

Weave ESG Knowledge Graph is a comprehensive database of ESG-related topics, issues, technologies, organizations, and relationships. It provides the intelligent discovery of ESG insights out of mountains of reports and facilitates intelligent ESG benchmarking, question-answering, and gap analysis.

How do you determine what issues are most material in each industry?

Our AI leverages the Weave ESG Knowledge Graph within a specific peer group benchmark and automatically unearths which ESG issues are most material in that peer group. This helps customers solve the “I don’t know what I don’t know” problem wherein they aren’t even aware of what ESG issues matter most in their industry or peer group.

How do you update the ESG knowledge graph and at what frequency?

The ESG Knowledge Graph is automatically built using natural-language-processing – by analyzing millions of ESG data points daily. The graph is updated 24/7.

Do you have scores that are restricted to a certain time window? For instance, can we create a benchmark that is restricted to just the last 12 months?

Yes, benchmarks can be created to only evaluate companies’ performance within a specific time period.

What is a smart talking point and why is it important in terms of materiality?

Traditional keyword-based relevance algorithms are very susceptible to greenwashing because they can be fooled by companies that merely pay lip service to a particular ESG issue without doing anything meaningful. Built by the team that created the core conversational AI algorithms behind Amazon Alexa, Weave.AI uses proprietary summarization algorithms to detect key takeaways (or smart talking points) and then employs deep learning to rank said key takeaways by materiality. To do this it employs proprietary language models that know the difference between an intent and an accomplishment, and how material a particular accomplishment is, and it does all this in the context of the industry in question. Furthermore, smart talking points are completely transparent – clicking on a smart talking point takes the user to the specific document and page where said company made that disclosure. This enables the user to learn more about the specific issue –right from the source.

What is semantic harmonization and why is it important in the benchmarking process?

Weave.AI performs semantic harmonization for use in analytics (gap analysis) and benchmarking. Without harmonizing semantics and context benchmarks can often be wrong. To take an example companies can talk about ‘diversity’ in a myriad of different ways and without semantic harmonization benchmarks and downstream analytics will likely mislead. By using proprietary AI language models and deep learning Weave.AI also understands context—and knows the difference between words like ‘waste,’ ‘fine’ and ‘strike’ in an ESG context and said words in a generic context.

What are report cards and how do they provide transparency?

Report cards are infographics that summarize a company’s ESG performance relative to its peer group. Report cards are deeply integrated with the Weave.AI ESG Knowledge Graph and indicate precisely where a company is under-performing or over-performing relative to its peers. These topics, called themes, are generated by the knowledge graph. Report cards are also fully interactive – the user can click on a particular peer to pivot from peer to peer, find out areas of underperformance or over performance, then click on those areas to determine the smart talking points corresponding to said areas. The smart talking points can then be clicked to navigate the user to the specific document where the company made said disclosure, and the specific page therein. This provides an end-to-end and fully transparent experience of ESG benchmarking and analytics – as opposed to opaque black boxes that don’t provide access to the underlying data.

Can I create custom benchmarks on subsets of ESG or adjacencies?

Yes. You can create benchmarks based on custom themes—such as Renewable Energy or a subset of ESG (e.g., the ‘E’, the ‘S’ or the ‘G’).

How do you help identify greenwashing?

Smart talking points are ranked based on materiality and distinguishes empty rhetoric from material accomplishments. In addition to analyzing corporate disclosures Weave.AI includes ESG webcast transcripts based on ESG-specific calls with Wall Street analysts. The themes are ranked not only by what the companies disclose but also on the questions ESG analysts are posing to the companies. Indeed, this analyst-provided insights are ranked as being more authoritative by Weave.AI’s materiality algorithms. Weave.AI also analyzes news and videos (updated in real-time) from the world’s top sources and these—weighted by the authoritativeness of the source—are also factored into a company’s performance rating. Weave.AI also uses these news articles and videos as inputs into its ESG Knowledge Graph—if, over time, the algorithm notices a significant issue in the news, it automatically adds that issue to the knowledge graph and updates the scores of the affected companies. Lastly, if our AI detects an ongoing discrepancy between a company’s public disclosures and a pattern of controversies as reported in authoritative news sources, this will negatively impact its score.

What if the entire industry is poorly performing? How do I create relevant benchmarks?

Even if the entire industry is greenwashing, Weave.AI can be used to ‘raise the bar’ by benchmarking the entire parent industry group or the sector, based on the GICS taxonomy. This will illuminate companies that are under-performing and/or greenwashing relative to a higher-performing peer group.

Does your ESG data meant to replace or complement existing datasets and tools?

We complement existing datasets and tools. We do not aim to replace your existing approaches. Most customers employ a multitude of tools and datasets to provide an overarching view of a company’s ESG performance. Asset managers also create propriety models using a variety of input datasets, including but not limited to Spectrum ESG.