The agent successfully gathered enough information to explain Marso Studio's core function, audience, pricing, and claimed differentiators by fetching the homepage and llms.txt. The site is moderately agent-ready: it publishes structured pricing and product info, but obscures critical usage details (per-asset credit costs, data storage duration) and contains an internally contradictory claim about its training data.

• ›The site publishes pricing and feature tiers clearly on the homepage, making that layer discoverable. However, it does not expose per-credit costs for assets or scenes, forcing users (or agents) to query a runtime endpoint (`GET /pricing`) or guess utility.
• ›llms.txt exists and contains accurate, well-structured product information (audience, integrations, pricing, differentiators). The agent discovered it by following a link from the homepage — it is properly surfaced.
• ›A critical contradiction surfaces when comparing the homepage and llms.txt: the homepage claims I2M is 'trained on measured real-world data,' but llms.txt states outputs are 'predictions, not measurements.' The agent caught this and flagged it, suggesting the site's own pages undermine each other.
• ›No comparison with competitors is published on the site. The agent correctly noted it cannot judge differentiation without external research, staying grounded in what was actually retrieved.
• ›Vague or undefined terms ('limited-period data storage,' 'seat-based credits,' 'Enterprise') appear in pricing tiers but are not elaborated on the site. The agent had to call these out as gaps rather than synthesize from model memory, maintaining fidelity to what was published.