Datasets, engine and rails

Training engine and workflow rails

Our proprietary self-learning process is rooted on our custom knowledge bases, Reinforcement Learning training engine and agent workflow rails. This is what allows us to go beyond training AI to generate content and instead teaching it to think strategically, mimicking the nuanced decision-making processes of human intelligence. Through this sophisticated RL framework, our personas learn to navigate complex engagement landscapes with remarkable precision

Our Reinforcement Learning model operates on a principle of continuous optimization. Imagine an intelligent agent that doesn’t just produce content, but constantly monitors its performance, adapting its strategy in real-time. When an action moves closer to the defined goal, the agent is rewarded. When it strays, it’s gently redirected — much like how a human learns from feedback and experience.

Seeding our knowledge bases

Each Memetica influencer is constructed around a dynamic, proprietary knowledge base that breathes and grows. Users can seed this foundation with public knowledge — think constantly updated news sites — and private data stored in cloud drives. But here’s where it gets revolutionary: this knowledge doesn’t just sit static. It evolves with every interaction, continuously reshaping itself through internal learning and external engagement.

Check our User Guide section for all the details on adding external data sources, both static and real-time feeds.