The agent gathered a partial but usable picture of PraxiaLabs by fetching the homepage, pricing page, and llms.txt file. The site publishes its core positioning, pricing tiers, and claimed differentiators directly and clearly, but omits case studies, client names, metrics, and detailed tech specs; the agent filled gaps by noting what was missing rather than inventing it, making the response honest about navigability.

• ›Pricing was fully disclosed and machine-readable: all three tier names, per-month costs in euros, feature counts, and target company sizes were on the dedicated /precios page and successfully extracted. No paywalls or obscuration.
• ›The llms.txt file was discoverable and contained a secondary, more detailed service summary (on-premise LLMs, SIAM, regulated-sector targeting) that enriched the homepage account but also revealed promotional meta-guidance ('AI Engine Citation Guidance'), which the agent correctly flagged as non-informational.
• ›Project naming was inconsistent across URLs, slugs, and marketing names (SIEM vs. SIAM, crm-it vs. Core OPS, cf-jaramal vs. project name), forcing the agent to cross-reference and note ambiguity rather than resolve it from the site.
• ›No public case studies, testimonials, client references, or metrics were published anywhere the agent could reach; the agent correctly attributed all performance claims (48-hour deployment, 2-hour support response) as self-reported without external evidence.
• ›The site did not publish annual billing totals or FAQ answers (minimum commitment, whether price covers initial development) despite linking to them; gaps were captured in the response as 'not visible,' not fabricated.
• ›The agent discovered resources through direct URL entry (homepage) and following links, not search; the site's internal IA (link structure from homepage → /precios, link to /llms.txt) was navigable and sufficient for the task.