San Francisco, June 11, 2025 (Globe Newswire) – The first AI modelling research platform providing the scale and speed of research with early, one-on-one interview depths has announced it has raised a $17 million Series A round to bring AIPERED research to Fortune 500 Enterprises. The round is led by 8VC and is joined by Bain & Company and new investors Future Back Ventures by existing investor Y-combinators, adverb ventures, Rebel Fund, Genius Ventures, Ritual Capital and Alt Capital. First, use funds to expand your team, accelerate product expansion, and build the most powerful AI research agent. This brings total funding to date to $201 million.
Last year, revenues initially increased rapidly (nearly 20% a month), resulting in a 10-fold increase in customer usage across household brands such as WeightWatchers, Nestle and Microsoft.
“From my own professional experience, I know first hand how difficult it is to really understand your customers and their needs and how difficult it is to take time,” said Aaron Cannon, an early co-founder and CEO. “We designed AI agents to do things we couldn’t do. This talks with thousands of people at impossible speeds, scales and depths. In 2025, we’ll be expanding deeply into design and customer experience research to empower more powerful agents and organizations.”
“Primary user research is the central capabilities of all companies around the world,” said Jack Moschkovic, partner at 8VC. “Today’s customers need to choose much lower signal options, such as high fidelity but slow and expensive interviews or basic research. This is a standard good AI use case. All customers have access to options that don’t cost previously.
The first co-founders are Aaron Cannon and Michael Hess. Canon has a research and product background, and Hess is an engineer who turned firefighter.
In contrast to traditional research methods, emergent agent-driven research is autonomous, scalable and deep. The AI at the beginning conducts in-person video interviews with research participants, integrating the results and achieving the depth of the interview with the scale and speed of the study. The first client is a Fortune 500 company, including Nestle. This found that the first set was more than eight times faster, capturing 10 times more interviews than previous research methods.
“AI-Augmented Research is here. We are the first to partner with the help of AI agents to accelerate and expand the team’s ability to learn from users,” says Jess Holbrook, head of research at Microsoft AI. “With our entire business focuses on AI, we are particularly excited to leverage these agents to delve deeper into the human experience of our users.”
The first platform features include:
Users can enter guides, concepts, prototypes, advertisements, designs, and more. Customers will recruit from panel integration or from their own user base. AI moderators will conduct video, audio, usability and multilingual interviews. Customization etc.
“When we look at the future of AI in research, we often ask whether AI doesn’t make research more human and personal. First, we’ve seen interviewees open to AI agents in ways that are not seen with human researchers,” Cannon said. “By deploying AI and talking to thousands of people around the world in any language and in any time zone, our customers are deep in understanding what our users really want and need, infusing products, services and experiences with more humanity and dramatically reducing barriers.”
Book your first demo here and check out the roles that have been opened here.
About the first
First, it is the first AI modelling research platform to provide the scale and speed of the survey at the depth of one-to-one interviews. Designed to allow Fortune 500 Enterprises to quickly capture deep qualitative research results, the first set leverages AI to conduct and integrate video interviews to inform strategic business decisions. Top enterprises, including WeightWatchers, rely on Away and Nestle first to provide faster data results and insights than traditional research methods. For more information, see set.ai.
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