Our Products

Distribute signals from your game engine using the Contextual Data Hub. Ensures efficient context distribution, customizable knowledge and perception schemas, power agent awareness with situational context construction.

Equip your NPC Agents with environmental information such as time and location sensitivity, event monitoring, which leads to environmental condition adaptation. Configure your NPC Agents to be aware of any custom change in your game environment, easily suited to a multitude of gaming scenarios.

Model a full spectrum of emotions with contextual responses and memory recall. Our NPC Agents utilize emotions and empathy to allow for sensitivity in responses. Allow for emotions to affect memory and recall of agents, which can shape the way their interactions take place.

We provide a flexible system to integrate custom actions available to NPCs within their environment. NPCs choose to use these actions to accomplish their autonomous goals.

Our agents function on a system of goals, which can either be defined by a developer, or dynamically remade and reprioritized through the lifetime of an agent. These goals will be pertinent to any overarching goals the agent may have throughout the scenario.

Facilitate both freeform and decisive conversations with varied interaction freedom using our conversational module. Effortless integration of contextual conversational memory.

Utilize goals and actions to define and control how the game world unfolds, and consequences propagate through Player or Agent interactions.

Dynamic relationship modelling which allows for evolving NPC Agent interactions across players or other NPCs. Combined with long-term-memory, we construct a structural representation of social groups, which is composed into a way the NPCs can interact with.

Create engaging narratives with dynamic dialogue options and story arc progression control. Employ fixed narrative constraints & triggers. Craft dynamic dialog options, which allow for control over how a structured story unfolds.

Utilize adaptive learning algorithms for behavior scoring and contextual feedback loops. Reinforcement based learning algorithms to condition NPC Agents with stimuli. Genetic algorithms to mutate current behavior, or behavior of offspring. Contextual feedback cycle which allows for dynamic behavioral changes based on the information gained across the lifespan of an agent.