MoEngage, a customer engagement platform powered by agentic AI and used by more than 1,350 consumer brands worldwide, has announced the acquisition of San Francisco-based Aampe, a company specializing in AI infrastructure that assigns an autonomous AI agent to each individual customer.

The deal represents an important step in MoEngage's evolution as a global SaaS provider. By integrating Aampe's reinforcement learning technology directly into its platform, MoEngage is creating a unified ecosystem where marketer-focused workflow agents and customer-specific decisioning agents work together seamlessly. This integration enables genuine one-to-one personalization at enterprise scale.

Traditional personalization strategies often reach a point where scaling becomes increasingly complex. As customer engagement programs expand, organizations must manage growing numbers of audience segments, customer journeys, experiments, and operational resources. Many existing decisioning systems also require teams to rebuild intelligence and context from scratch whenever new campaigns or initiatives are launched, limiting long-term learning and efficiency.

MoEngage has strengthened its position across Europe, helping consumer brands address stricter data privacy requirements while meeting rising customer expectations for relevant and personalized experiences. Aampe's privacy-first design complements this mission by avoiding the storage of personally identifiable information and instead relying on anonymized behavioral data. This approach aligns with modern data minimization principles while still enabling individualized customer engagement. As MoEngage expands globally, the company continues to support brands in delivering highly personalized interactions at scale.

"Every marketer aims to reach customers with the right message at the right time," said Raviteja Dodda, Co-founder and CEO of MoEngage. "The real challenge has always been having the infrastructure to make that possible. Aampe has developed a capability that continuously optimizes content, timing, communication channels, and message frequency for every individual user. Equally impressive is the expertise of the team behind it. Paul, Schaun, Sami, and the entire Aampe organization combine deep research knowledge with the operational discipline needed to solve one of marketing's most difficult challenges. Together, we are shaping the future of agentic marketing."

MoEngage has already invested significantly in AI-driven marketing through its Merlin AI platform. Aampe enhances these capabilities with advanced agentic decision-making technology that has been tested by some of the world's leading consumer brands. Each AI agent independently determines what content to deliver, when to send it, how frequently to communicate, and which channel to use, while continuously learning from customer responses. Marketers retain control over content, objectives, and operational boundaries, while the AI manages execution and provides full visibility into its decisions.

Paul Meinshausen, Co-founder and CEO of Aampe, explained the company's vision: "We built Aampe around a simple principle—one agent for each user rather than one model for an entire segment. Each agent develops an ongoing understanding of an individual's behavior, preferences, and engagement patterns. Because the system learns from underlying meaning rather than isolated messages, knowledge accumulates over time instead of resetting with every interaction. By joining forces with MoEngage, we gain the infrastructure, channel capabilities, and customer reach needed to make this approach widely available."

Following the acquisition, Aampe's founding leadership team—Paul Meinshausen, Schaun Wheeler, and Sami Abboud—will join MoEngage to spearhead its Agentic Decisioning initiatives. Existing Aampe customers will continue to receive uninterrupted service while benefiting from MoEngage's expanded engineering, data science, and customer success resources.

Aampe's technology is already being used by prominent consumer brands such as Taxfix, ZenBusiness, Grab, and Swiggy. The platform currently operates hundreds of millions of dedicated AI agents and evaluates more than 200 billion decisions every week.

One example of the technology's effectiveness comes from Taxfix, a leading European AI-powered digital tax management platform. Instead of relying on conventional segmentation and predefined customer journeys, Taxfix deployed Aampe's model of assigning a dedicated AI agent to each user.

Alex Beresford, Chief Growth Officer at Taxfix, noted that Aampe outperformed the company's rule-based CRM system—which had been refined over four years—by 50%. The deployment also generated a 40% increase in revenue compared with a global control group and achieved break-even within a month. According to Beresford, the cost efficiency of Aampe was between 120 and 150 times greater than advertising spend used to drive comparable customer retention outcomes.

The acquisition also introduces MoEngage's "Start Anywhere" model. Brands can integrate Aampe's individual-agent technology into their existing marketing automation or customer engagement platforms without disrupting current operations. Existing MoEngage customers, meanwhile, gain direct access to Aampe's capabilities through native integration. In both cases, advanced individual-level decisioning becomes available without requiring major platform changes.

Additionally, the merger brings together the AI research teams of both organizations under a shared mission: advancing the next generation of agentic marketing. By combining Aampe's research expertise with the large-scale production environment provided by MoEngage's global customer network, the companies aim to accelerate innovation and expand the practical applications of AI-driven customer engagement.

• Artificial intelligence (AI) and machine learning (ML) are expected to be foundational elements of the 6G standard, anticipated around 2028–2029.
• Engineers working at the intersection of 6G and AI can accelerate time-to-market by understanding how AI/ML can support 6G design and validation.

• improved channel state information for UM-MIMO
• more accurate beam prediction
• reduced complexity in beam pairing
• optimization of the environment using reconfigurable intelligent surfaces (RIS)

• RAN optimization and mobility management
• beamforming prediction
• automated functional testing using RL-trained agents
• detecting performance bottlenecks through large-scale exploration

• advanced channel estimation in challenging environments
• channel state information (CSI) compression via CNN-based autoencoders

• Digital twins: full-scale virtual replicas of networks
• Generative AI: GAN-based wireless channel synthesis
• Specialized testbeds: simulated sub-THz scenarios
• Propagation simulators: ray-tracing tools that mimic real-world environments
• System-level tools: integrated platforms that combine analytics, noise, and channel models to produce training datasets

• AI-based nonlinear models for complex behaviors
• integration of AI into EDA tools for RFIC design
• testing and evaluating AI-enabled physical-layer blocks
• AI-enhanced beamforming and CSI compression
• hardware-in-the-loop testing with channel emulation
• anomaly detection during simulation and validation

• Ensure AI aligns with established wireless engineering principles
• Plan for limited real-world data by augmenting with analytical models
• Use interpretable AI methods alongside black-box models
• Apply physics-informed constraints to maintain realism
• Prevent overfitting through proper data diversification
• Use hardware-in-the-loop testing to close the gap between simulation and reality
• Mitigate energy, security, and operational risks introduced by AI integration