Signal AI, an artificial intelligence startup that trawls the vast sea of internet and other publicly available data to provide organizations with sentiment insights and other information to make better business decisions, has raised $50 million. It plans to use the funds to continue building out its AI platform to bring in more diversified data sources, in order to extract insights across an ever-wider range of business questions that a person might ask.
“Organizations still don’t have an effective radar to get ahead of threats and opportunities, and turning challenges into opportunities,” said David Benigson, the startup’s CEO, in an interview. They aggregate hundreds of sources of data — from social and news media through to 25,000 podcasts, regulatory filings and other public records — into a single platform.
It then applies machine learning and other AI techniques to extract insights from it all based on natural language questions posed by Signal AI customers. “We’ve been diversifying the data that we inject in the platform,” he added. Signal AI currently works across some 100 languages.
The funding is coming in the form of a Series D, and it is being led by Highland Europe, with new backer abrdn plus previous backers Redline (which led Signal AI’s Series C in 2019), MMC and strategic backers Hearst and Guardian Media Group Ventures also participating. London-based Signal AI has now raised $100 million. It is not disclosing its valuation, but Benigson — who co-founded the company with Miguel Martinez, the company’s chief data scientist — said it grew 100% over the last round.
PitchBook estimates that valuation was around $100 million at that point (in 2019), which would put it at $200 million now, if that figure is accurate. In any case, Signal AI itself has definitely grown. Benigson said that the startup now works with 40% of the Fortune 500, with its customer base including Deloitte, Bank of America and Google.
The challenge that Signal has identified and is building to fix is one that we all encounter every day, but feels particularly acute when it involves businesses navigating tricky issues with potentially billions of dollars of investment at stake if they take the wrong turn.
The internet has equipped us with a vast trove of information, but not always the best keys and maps for unlocking and getting around it, especially when the answers we are looking for are not straightforward, as they are in the case of more fluffy questions around sentiment analysis, or “answers” that are actually a collation of information from a number of sources.
There are a number of companies that have also identified this gap and are building to solve it, including Dataminr (which raised a huge round this year at $4.1 billion valuation), Meltwater (publicly traded and also acquiring businesses to build out its technology muscle) and Cision (now privately held and also making big acquisitions to grow).
Much of the emphasis and impetus for these companies started and still sits squarely in the area of media monitoring — a huge business tapped not just by other media sources but by companies themselves. Indeed, Signal AI itself used to be called Signal Media and focused mainly on this area as well.
That in itself is still a big market that is worth disrupting — the old-school way of approaching this was to collate media clippings, provided to clients; the new approach is not just to collate mentions but to deliver more summarized information and insights gleaned from those clips. The more the internet grows, the more clips there will be, and so in fact simply getting a pile of them becomes untenable for even the most enthusiastic teams of communications specialists.
Even so, that model set up for more intelligent media monitoring also paves the way for applying the same format and algorithms to a much wider set of use cases, which is the premise Signal AI has been building upon.
A large part of its work, thus, still remains focused on providing insights to people working around communications strategy, but it’s deepening the types of information it can provide to them by way of its AIQ platform (as Signal calls it).
Benigson notes that this now includes, for example, more information and “insights” for a company on potential business partners; getting up to speed on an aspect of diversity and inclusion and how it’s being approached by others; a pending decision on environmental strategy; and data to inform a company’s strategy on regulatory compliance in areas like taxes or data protection.
While comparisons to companies like Dataminr are fair, Benigson said, Signal AI differs from these as it provides more context both in the kinds of queries that can be asked by users, and in the responses that are given. Alongside its business growth, that richer experience is another reason why investors are interested in seeing how the startup will grow.
“Signal AI is a stand out category defining business” said Tony Zappalà, a partner at Highland Europe, in a statement. “We are excited to be involved with the next chapter of the company’s innovative growth. David and the leadership team have a clear vision for the decision augmentation category they are helping to define and as Gartner’s research has shown, the opportunity is huge.”