While companies building consumer apps and prosumer tools invest heavily in personalising user experiences through product usage data, teams still manually craft the workflows that deliver those personalised moments.
In this regard, Aampe has deployed over 100 million intelligent agents into consumer applications running across four continents.
Businesses that have deployed Aampe agents include some of the leading food delivery and on-demand apps in South and Southeast Asia, top sports and fitness apps in Europe, as well as major fintech and entertainment apps in the U.S.
The agents are managing on the order of 15-200 billion decisions every week that determine product surface interactions.
The company is announcing $18 million in Series A funding led by Theory Ventures bringing Aampe’s total funding to $27.3M to accelerate the adoption of its agentic infrastructure. Z47 is also participating in the round.
Conventional approaches to personalising digital products have relied on humans manually creating rules and segments to determine what users see and when.
This approach — unchanged for over a decade — requires teams to manually orchestrate the message or product surface that will best serve the end user’s interests, whether they’re making a purchase, evaluating content options, or trying new features.
With consumer preferences rapidly and continually changing, the conventional approach creates a massive human bottleneck and non-scalable operational workload.
Aampe’s infrastructure takes a fundamentally different approach: deploying a unique AI agent for each user that continuously learns from interactions and intelligently decides what to show, when to show it, and most importantly, whether to show anything at all.
Designed to continuously monitor usage and engagement data, each agent skillfully observes and learns the user’s changing preferences. Agents are then responsible for translating inferences into optimal management of the user’s interactions with the product — enabling genuine 1:1 personalization even for products that serve tens of millions – or more – users every day.
“Consumer applications today almost universally look the same to everyone who opens them, with personalization limited to narrow recommendation feeds,” said Paul Meinshausen, CEO and co-founder of Aampe.
“We’ve designed and developed infrastructure that enables every aspect of an application to adapt to each user’s context and preferences, continuously. Our mission is to fundamentally improve the way users experience digital products.”
Founded in 2020 by a trio of scientists, Aampe emerged from a unique combination of expertise. Meinshausen, who previously co-founded PaySense (acquired by Prosus/PayU for $185M), met co-founder Schaun Wheeler in a U.S. Army Intelligence Analysis unit in 2009.
Along with Sami Abboud, a former semiconductor engineer and neuroscience PhD, the founding team combines backgrounds in cognitive and behavioural science, engineering, and experimentation. They’ve harnessed their specialised backgrounds to design a new AI architecture for user interaction.
Rather than using traditional machine learning or generative AI alone, Aampe’s infrastructure leverages a subset of AI called reinforcement learning to enable continuous, parallelised experimentation.
Each agent learns and adapts in real-time, helping their user manage their attention and make complex choices in a world of material and content abundance.
The agents operationalise their decisions by intelligently managing a range of existing product and marketing tools – including data platforms and warehouses, marketing delivery platforms, and product analytics tools, allowing companies to extract more value from their current technology investments.
Alexander Beresford, CGO/CMO at Taxfix, says “Customers now expect brands to know what they want and respond instantly – standards have gone up. The future of engagement in owned media lies in AI systems that learn from each customer’s behaviour and adapt automatically to deliver personalised experiences.
“Unlike older systems that follow rigid rules, these AI agents evolve with the customer, keeping every interaction relevant without extra effort from the business. This isn’t just a new trend – it’s where everything is headed.
“For brands looking to stay competitive, adopting this approach isn’t optional; it’s the difference between sounding irrelevant and sounding like you understand them. Aampe is for me a leap in that direction which brings a novel approach to individual customer needs.”
“AI agents can make decisions at a scale that is impossible for any human,” said Andy Triedman, partner at Theory Ventures. “Aampe allows customer engagement teams to craft experiences for their diverse user base, versus just one or two flows targeted at the typical person. This new type of infrastructure will be transformational for companies looking to provide personalization driven by data.”
Aakash Kumar, managing director at Z47 added: “The world of app engagement has not delivered on the promise of deep learning-led personalisation. Agentic AI provides the opportunity to break through. Paul and the team at Aampe are shaping the future of agentic infrastructure for user journey personalization, with excellent feedback and adoption from their early customers.”
The company’s privacy-centric approach, using zero-PII storage practices and anonymised behavioural patterns, has already attracted major consumer businesses across Southeast Asia and North America.
The company has already deployed over one hundred million (100,000,000) agents for enterprise customers across 4 continents.
As Aampe scales, it plans to double its team by the end of 2025, focusing on helping enterprise customers successfully migrate their workflows and adopt agentic infrastructure into their organizations.
Looking ahead, Aampe aims to power the next generation of consumer applications through its easy-to-deploy agentic infrastructure.
While their earliest applications focused on marketing and messaging channels, Aampe has been rapidly extending its agents capabilities to manage the entire user experience—from interface layouts to feature discovery—enabling every interaction to adapt continuously to every user and their preferences at any given point in time.
