Entity-Specific Onboarding.
Marxi is not deployed with generalized industry knowledge. Each entity is onboarded individually, combining internal team knowledge with public data into a dedicated knowledge graph before any output is generated.
Two Sources, One Entity-Specific Graph
Onboarding draws from two inputs simultaneously. The first is internal: the working knowledge held by the team and ownership behind the entity, including its niche, its value proposition as the business itself defines it, its competitive position, its operational limitations, and its scaling expectations. The second is external: public data on the entity's digital footprint, market reputation, and competitive standing. Marxi resolves both into a single entity-specific knowledge graph before any output is generated or any agent begins operating.
No Category Averages
The model is not designed to reason from what is typically true of a business in a given vertical. It is designed to reason from what is specifically true of the entity it was onboarded for. Output is calibrated against that entity's own documented parameters rather than industry generalizations, with the goal of producing recommendations and content with no generic filler and no assumptions borrowed from category norms.
Deployed as an Internal Function, Not a General Tool
The model is built to operate as a specifically trained internal extension of the entity itself, its vision, its niche, its value proposition, its market share, and its constraints, rather than as a generalized AI system pointed at a business from the outside. Agentic execution only begins once this entity-specific foundation is established.