Why we invested in Attention
I’m excited to share the news that Eniac led a $3.1 million seed funding round in Attention last year, which was announced today in TechCrunch.
We love working with repeat founders — and in this case, Attention’s Anis Bennaceur (CEO) and Matthias Wickenburg (CTO) actually used to compete against each other at their respective startups Mixer and Swipecast.
When they reconnected five years later, they realized that they’d faced similar pain points on the sales side: making sure Salesforce was consistently and accurately updated, and getting sales reps started quickly. (The latter is a challenge Alloy’s Laura Spiekerman recently discussed with founders in our portfolio.)
To address these issues, Attention takes unstructured data from sales calls (whether they’re on Zoom, Meets, Microsoft Teams, or dialing software) and uses generative AI to turn them into a utility for the sales team. It’s able to draft follow-up emails based on call data and the sales rep’s instructions, and can also present the rep with battlecards that help them handle sales conversations in real-time — for example, if a prospect asks about how their product stacks up against the competition, a battlecard could appear with talking points.
Beyond the impact we believe Attention will have in making sales teams more effective, we also believe the team is at the forefront of important AI and natural language trends that have been a core investment thesis for us over the last few years. First, the company shows how natural language understanding and generation will allow humans to interact with computing without changing their behavior — instead of requiring users to use a computing interface, they can communicate naturally and have computing understand them. This should grow the entire addressable market for computing.
Second, we believe that there will be a proliferation of AI generation tooling as we saw with natural language understanding. This will lead to tools offering lower level hooks in order to attract developers. Once that happens, startups like Attention can build competitive moats by customizing these models for their particular use case and training them on proprietary datasets. For example, as Attention establishes itself as a leader and has the most usage, it should be able to write the best follow-up emails from a sales call.
I should add that both the Attention and Eniac team believe it’s best not to rely on the above scenarios playing out. Despite their transformative potential, we recommend that AI generation-based startups still build moats in traditional ways, like becoming a standard or leveraging network effects in order to ensure they are not commoditized over time.
So for all of those reasons, we can’t wait to see what Anis, Matthias, and Attention do next!
