Aampe

Time for a #FridayFYI ??: Scaling AI Agents Isn’t Hard. Making Them ?????????? at Scale Is. One of the biggest challenges Aampe originally faced wasn’t just scaling the number of agents. It was scaling their ability to make complex, real-time decisions efficiently. But now with over 100 million agents deployed across four continents (and every user getting its own individual agent), Aampe is leading the way for the future of agentic infrastructure built for effective, continuously adaptive, and scalable user engagement. Most generative AI applications today are B2C. The AI interacts directly with an end-user, who interprets its response, adjusts accordingly, and gives feedback. It’s a neat little feedback loop ??. But enterprise AI is a different beast. Our agents don’t just chat with users; they ?????? on behalf of businesses, making decisions that have real impact. That means they need to be razor-sharp in picking the right message, timing, and content without endless trial and error. Here’s the crux of the problem: once an agent decides on who to message, when to message, and what to recommend, it still has to select the right content that fits all those choices. That’s where scale gets tricky. We could have brute-forced our way through this by testing every possible combination. But that’s slow, expensive, and impractical. Instead, we designed our agents to think smarter, not harder. ?? First, they determine the single most important factor for each user—maybe it’s the tone of voice, maybe it’s the call to action, maybe it’s the time of day. ?? Then, they narrow the content choices based on that priority—instead of wasting time sorting through everything. ?? Finally, they make their selection from a much smaller, more relevant set. This approach makes decision-making fast, efficient, and scalable—without sacrificing relevance. Scaling AI agents? Easy. Scaling AI judgment? That’s where the real work happens. Would love to hear—what’s been your biggest challenge in scaling AI? ??

The future of food delivery isn’t just about what’s on the menu—it’s about what’s on your mind. For the past decade, “big data” has operated under a simple rule: garbage in, garbage out. A food delivery app, for example, has always relied on two primary data sources: 1?? Restaurant & dish data – Addresses, menus, prices, ratings, and tags like “vegetarian” or “spicy.” 2?? User data – Search history, past orders, and browsing patterns. Sounds robust, right? Not really. The missing piece? ?????????????? ?? . A food delivery app today knows ???????? you ordered. But it has no real clue ??????. Was it a high-protein meal to hit your fitness goals? A nostalgic dish from childhood? A pick for date night? Conventional data models don’t capture intent. Enter AI agents. Instead of relying on static, pre-existing data, AI agents learn dynamically. They don’t just record transactions. They infer, adapt, and curate experiences based on real-time interactions. Imagine a food delivery AI that recognizes patterns beyond “You like sushi.” Instead, it understands: ?? You crave sushi after long workdays. ?? You love spicy food—but only on weekends. ?? You’re trying to eat healthier, so it nudges you toward better options. The old world of data schemas locked us into rigid categories. AI agents break free, evolving their understanding of users in a way that’s deeply personal. The next frontier of AI isn’t just about recommending what’s available—it’s about anticipating what matters. What do you think? How else could AI-driven personalization change the game? ??

Who will be joining us at MAU Vegas in May?? ?? Let us know in the comments!

Reinforcement Learning is Like Finding Your Favorite Ice Cream Flavor ?? At first, you start with vanilla. Safe choice. Solid. But then—chocolate. Game changer. Next, strawberry—pretty good, but not chocolate-good. Over time, you try more flavors, forming a mental ranking of what you love, what’s just okay, and what’s a hard pass. That’s reinforcement learning (RL) in action: trial, feedback, adjustment. Every time you experience something new, you update your preferences. Aampe’s RL-driven agents learn the same way. When they first interact with a user, they start with educated guesses, just like how you might default to vanilla if you’ve never had ice cream before. But then they send a message on a Thursday afternoon. The user clicks! Suddenly, “Thursday” and “afternoon” get a little score boost. And the message was about new sneakers? “Product category: shoes” and “Value prop: new arrivals” just climbed the leaderboard too. But what if the user ignores the message? Those scores fade. What if they engage with a different offer at a different time? The agent adjusts. Just like one day you might randomly try pistachio, only to realize, “Wow, I’ve been sleeping on this flavor.” This is where RL shines over traditional personalization. Instead of relying on static rules (i.e., “You liked chocolate once, so that’s all we’ll ever serve you”), Aampe’s agents adapt in real time. If a user’s behavior shifts—maybe they change jobs, their style evolves, or they suddenly develop an obsession with hiking gear—our system doesn’t just notice. It responds. The magic of RL isn’t about predicting the future; it’s about staying in sync with the present. Humans are messy, unpredictable creatures. But we’re also natural tinkerers, constantly experimenting to see what works best. That’s exactly what Aampe’s RL-powered agents do—learning, evolving, and always ready to serve up the next best flavor. So, what’s your "chocolate moment" in personalization? Let’s chat.

Moving Beyond Traditional Segmentation: How Agentic AI Redefines Personalization Traditional segmentation, though effective for its time, is inherently limited by its rigid, predefined structure. Marketers define rules, place users into static segments, and hope the assumptions behind those rules hold up across changing behaviors. Worse, this approach offers little visibility into how or why users are bucketed, making it hard to adapt when customer needs evolve. Agentic AI represents a profound shift in personalization. Rather than assigning users to static groups, AI agents can continuously build an evolving, multi-dimensional profile of each individual. This enables them to learn directly from behavioral patterns, not just broad attributes, uncovering the nuanced decisions that drive human actions. Imagine every user having their own virtual “agent” that dynamically evaluates their preferences, history, and behaviors in real time. It’s like adding headcount for every single user, ensuring no opportunity for personalization is missed. With this framework, when a new user joins with similar behaviors or preferences to existing users, agents don’t rely on guesswork. They leverage a deep, adaptable understanding of other users' patterns to make inferences that are both personalized and predictive. By shifting from static segmentation to an agent-based approach, marketers can move beyond the limitations of fixed categories to achieve personalization that feels as fluid and dynamic as the decisions humans make daily. This is the future of personalization—rooted in real decisions, adaptive intelligence, and ever-evolving user understanding. It’s not just segmentation; it’s real personalization at scale. Read the full blog post here: https://lnkd.in/eCBhA_ja

Are we ready to let machines not just assist us, but make decisions for us? ?? This is a question our founders, Schaun Wheeler and Paul Meinshausen, tackled in a recent webinar. They explored Agentic AI, a future where machines automate decisions, not just tasks, to deliver better outcomes at scale. We hear this from customers all the time: businesses still spend too much time ???????????????? making repetitive decisions—like what email to send, which product to recommend, or how to personalize offers. Even with AI, most tools stop at insights, leaving humans to connect the dots. Agentic AI changes the game by automating both the decision-making and learning process, freeing teams to focus on strategy and creativity. As Schaun and Paul explained, decision-making comes down to three critical elements: ??Decision Set: What options are available (e.g., send a discount, choose the right offer, or hold back)? ??Outcome Set: What happens based on that choice (purchase, engagement, churn)? ??Information Set: What data is used to make (and improve) these decisions? This framework ensures businesses move away from ad-hoc, manual decisions and instead recognize repeatable, systematic decision-making opportunities—especially in high-frequency, constrained domains like marketing. But the problem with many tools today is that they treat every decision as unique, forcing human teams to ???????????????? optimize over and over. Whether it’s messaging systems built on "campaigns" or static content management systems for apps and websites, they ignore opportunities for machines to learn, iterate, and optimize decisions autonomously. Here’s where Agentic AI really shines: leveraging behavioral data to infer user preferences and improve outcomes. Aampe’s approach is particularly interesting as it recognizes that businesses need to shift from simply automating campaigns to automating and optimizing the ?????????????????? behind them. For example, the decision to send a discount isn’t just about offering 10% off vs. 20%; it’s about understanding the context—inventory levels, user preferences, and even business priorities. Decision automation isn’t just about efficiency; it’s about ??????????????????????. By narrowing decision sets, evaluating outcomes, and building systems that learn from repeatable decisions, businesses can achieve real optimization. Think fewer “manual campaigns” and more continuous, personalized engagement that aligns with both user preferences and business goals. At Aampe, the team is pushing beyond traditional AI models to empower businesses to build smarter, faster, and more adaptable systems. This isn’t just about adopting technology, it’s about rethinking how decisions get made. So, are we ready to get comfortable with machines making decisions for us? If the future is Agentic AI, I’d say it’s time we start. Let's hear your thoughts in the comments! Check out the full video here: https://lnkd.in/ewT_RwTJ

Exciting news for Aampe! We just closed an $18 million Series A funding round led by Theory Ventures. Over the last 14 months, we've deployed over 100 million AI agents to transform how enterprises in food delivery, retail, travel, and consumer fintech create personalized digital experiences that engage users in their own context. Aampe was founded on a belief that consumer businesses should continuously make the strongest possible inferences about their customers and dynamically respond to their changing preferences and interests. Whether it's outbound messaging or in-app screens, Agentic Infrastructure represents a paradigm shift in personalization – enabling marketing and product teams to deploy agents that build stronger, more meaningful customer relationships. We couldn't be more excited for the next phase of our growth and deeply appreciate all our followers - investors, customers, and well-wishers - for believing in our vision!

See, how Aampe personalizes messages and experiences? Very demure, very mindful! Aampe doesn't think in terms of segments, instead, it focuses on people, chooses the best alternates that our users find cutesy, and delivers the messages at the right time – very mindful, very demure!???

Aampe is hosting a mini-series of live masterclasses on Recommender Systems starting next week! The first session will have DJ Rich and Schaun Wheeler talk about what really matters when implementing a Recommender System. Arpit Choudhury will orchestrate ?? If you search for information on Recommender Systems, everything has been SEO-optimized to talk about general principles – collaborative filtering, content-based recommendations, etc – and delves into specifics like matrix factorization. None of this helps a business get a Recommender System up and running. This session has been designed specifically for product and growth practitioners to learn what really matters when implementing a Recommender System. When? August 14, 11:30 am ET (3:30 pm GMT) Register now: https://lu.ma/7i9sgzdz

Accenture recently published a fascinating and powerful research report on "Cutting through the noise in consumer experience". They reported that "74% of consumers abandoned their shopping baskets in the last three months simply because they felt bombarded by content, overwhelmed by choice and frustrated by the amount of effort they need to put in to making decisions." At Aampe we know that THE fundamental problem for every consumer business today is effectively and empathetically managing their customers' attention. We live in an attention-scarce economy and the businesses that take care of their customer's 'attention account' will be the ones that will sustainably access their customer's financial account. With Aampe's Content and Message Catalogue capabilities, your CRM and product teams can finally engage every single user in their own context, with the content that reduces their stress, and improves their buying journey. Reach out today for a demo and to find out more!