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How CB Insights uses AI
Ben Tossell
February 29, 2024
👋 Hey, this is Ben with a 🔒 subscriber-only issue🔒 of Ben’s Bites Pro. A weekly newsletter covering how businesses are using AI.
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I write about businesses using AI and today I’m excited to show you how CB Insights is using AI.
CB Insights is a tech market intelligence platform that provides data and analytics on private companies and emerging technology trends.
Thank you to CEO Anand Sanwal, CPO Nichelle Dekeyzer, and CTO Mike Ruggieri for spending time with me and sharing their thoughts.
Here’s a quick breakdown:
Creating a cross-functional AI tiger team
Encouraging adoption through Hack Days & Lunch and Learns
Launching new AI-powered products
How AI boosts their current offering
Ways in which AI can transform the customer experience
Reimagining how they capture and create data
CB Insights has been using machine learning for several years.
This 2014 post mentioned that 70% of their data collection was from their machine learning software, with the remaining 30% from direct submissions from investors.
We’ve built machine learning software which parses unstructured and semi-structured data sources and programmatically extracts the key pieces of structured data we care about. Things like company & investor/acquirer name, amount of funding, valuation, date, stage, board of directors, patents, etc.
The core of this data is extracted from crawlers that analyze 150,000+ sources on a daily basis. In the last 8 months, we have crawled and analyzed nearly 16 million unique articles and information sources. The list of sites we index grows regularly but includes the following mostly unstructured data sources:
Regulatory filings – form Ds as an example
Investor websites (press & portfolio pages)
Company websites (press pages & blogs)
Acquirer websites (press pages or investor relations sections)
Press releases
Social media (Twitter primarily)
A select group of local, national and international news and trade publications
This was pre-generative AI. Now they’re integrating AI internally and within their product in several ways.
Forming a Cross-Functional AI Tiger Team
CBInsights formed a "Tiger Team" that brought together top performers already doing innovative AI projects across the company.
The tiger team was born out of a desire to innovate ways of working quickly across multiple teams. Like many companies, we found that we had pockets of genius throughout the organization where really incredible and innovative work was happening with GenAI, but that work was being done in a silo.
The tiger team framework allows us to bring together very cross-functional groups that were already innovating in their functional areas to work together on high-impact, company-wide problems.
Nichelle Dekeyzer, CPO
The Tiger Team is split into three workstreams.
Content creation
Research
Sales automation
But they continue to bring the whole group together weekly to share learnings which influences work happening in other projects.
One of the first initiatives was a dramatic overhaul of the content creation process. They turned long-form research briefs into easy-to-digest content for customer outreach.
Traditionally, it would take four days.
Now it’s less than 1 hour.
They used ChatGPT (GPT-4) to condense research briefs into < 60-second scripts. Then, they used WellSaid Labs for lifelike voiceovers and Jitter for the video. Here’s the output - “Why Flexport’s valuation might have dropped by 80%”.
"Our content creation team focused on creating short videos... The entire process was done using generative AI and took video creation down from 4 days to less than 1 hour from start to finish,"
The customer insight group developed a Slackbot that delivers relevant market and competitor research to customer success reps in under 30 seconds instead of 2-10 hours of manual search.
The bot makes individualised recommendations on relevant markets, companies, or competitive insights.
For example, our customer success team can input a user into the bot and receive back a Market Report or Feed recommendation that would be beneficial for that specific customer.
The sales automation group also benefits from this research tool.
On the flip side, the research team or sales team could feed the slackbot a piece of research content and get a list of users who are likely to care about this research.
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