This announcement marks a fundamental shift from training AI models based on the preferences of a single human user to training them on real business outcomes measured through actual campaign telemetry, conversion data, and KPI performance aggregated over time. This breakthrough addresses the critical gap between AI that generates engaging content and AI that drives measurable business outcomes such as return on advertising spend (ROAS), customer acquisition costs, and long-term customer lifetime value.
This is one of the first successful applications of testable reward learning principles to non-deterministic business metrics at production scale. While previous breakthroughs in reinforcement learning focused on deterministic outcomes, such as math problems with a clear right answer or code that either works or doesn’t, Marketeam has cracked the code on training AI models to optimize the messy, delayed, multi-objective reality of business performance. This is not just an incremental improvement. This is a whole new paradigm for how AI impacts real-world outcomes.
From mathematical certainty to business reality
The RL-KPI framework builds on the recent success of reinforcement learning with verifiable rewards (RLVR), which has driven breakthroughs in mathematical reasoning models such as DeepSeek-R1 and Tülu 3. However, whereas RLVR relies on deterministic validators that provide immediate binary feedback, RL-KPIs operate in a world of entirely different complexity, where success metrics are probabilistic, delayed and influenced by weeks or months. It changes based on external factors and requires balancing multiple competing goals simultaneously.
Marketing is the perfect testing ground for this breakthrough because it embodies all the challenges of real-world business optimization. Conversion data can take 14 to 90 days to mature according to Google’s attribution model, success requires balancing competitive metrics like brand safety and performance, and market conditions are constantly changing. This technique successfully handles sparse reward signals, temporary credit allocation over extended periods of time, and multi-objective optimization under uncertainty.
Driving rapid customer growth and market validation
Breakthroughs deliver transformative results for customers across a variety of industries.
- Glassy Baby: For artisan glass companies, IMEs serve as full-fledged operators. Think through your brand’s unique mission, build campaign structure, execute creative, and execute media buys. Through continuous optimization, RL-KPIthe system will drive down inexorably CAC Grow conversions while preserving your brand’s unique identity and mission.
- INKEY list: In collaboration with global skincare brands, Marketeam manages the full range of new search frontiers. Within 90 days, the Inkey List team was able to accomplish the following goals: 2.5x growth in high-intent, high-converting organic traffic Leverage IME’s AEO/GEO and SEO modules to enable your brand to serve as the primary “ground truth” for AI answer engines and legacy searches.
- At an enterprise scale, Global consumer goods conglomerate Provided using IME Prediction certainty across different brands. Multiple teams use the platform to execute creative direction through predictive analytics and identify optimal audience segments and campaign trajectories before execution. The system further streamlines influencer operations by automating brand safety reviews and summaries, while supporting product groups with data-driven ideas. This ensures that every creative decision, from influencer matching to product positioning, is guided by the brand’s global business pillars and customer-first brand values.
The customer value creation is significant, with Marketeam.ai achieving 14x growth in less than 12 months by consistently delivering an average ROI of 6x to its customers. This organic growth trajectory supports both technological advances and the emergence of entirely new market categories.
Create an AI native IME category
Marketeam.ai has established Integrated Marketing Environments (IMEs) as the next major category of marketing technology. Unlike traditional marketing tools that fragment workflows across multiple dashboards, IME operates marketing as a single autonomous system.
“We are witnessing the emergence of true AI-native marketing,” explains co-founder and CEO Naama Mannova Twito. “AI in marketing is not new, but it has remained at a blanket assistant level and highly fragmented with no accountability for real outcomes. We have been building marketing intelligence from the ground up to understand business strategy, optimize for real outcomes, and operate autonomously at scale. The RL-KPI breakthrough is what makes this possible. It’s the difference between AI that drives conversations and AI that drives conversions.”
Production-scale implementation with NVIDIA AI technology
This breakthrough leverages the NVIDIA AI infrastructure software stack to enable enterprise-scale deployments. The NVIDIA NeMo RL Open Library provides the foundation for reinforcement learning through advanced RL algorithms such as GRPO (Group Relative Policy Optimization) and DAPO (Direct Advantage Policy Optimization) and optimized RL training at scale. The implementation includes the NVIDIA NeMo Curator open library for curating marketing intelligence datasets, Ray-based orchestration for distributed training across multiple nodes and GPUs, and NVIDIA TensorRT-LLM optimizations for production inference with the introduction of NVIDIA NIM.
Additionally, Marketeam.ai’s Marke Thinking 8B foundational model, trained on over 10 billion tokens of curated marketing intelligence, shows that domain-adaptive models in the 1B to 8B parameter range consistently outperform much larger general-purpose models on business-critical marketing tasks when trained with the RL-KPI methodology.
Industry impact beyond marketing
While marketing serves as a testing ground, the RL-KPI breakthrough has significant implications for any business domain where AI systems need to optimize measurable outcomes in an environment of delayed feedback, multiple goals, and uncertainty. Financial services, healthcare operations, supply chain optimization, and customer service automation all share similar characteristics that make them candidates for RL-KPI applications.
The company plans to release comprehensive technical documentation after the GTC session to enable broader adoption of its business outcomes-driven AI training methodology. Future development will focus on extending attribution modeling capabilities for longer business cycles and extending the framework to additional enterprise domains.
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