The agent could not access m-ia.app directly but assembled a partial understanding from search results and cached web pages. It successfully identified M&IA as an agentic AI platform for M&A professionals focused on DCF, LBO, and due diligence workflows, but failed to retrieve critical information about pricing, company background, founding details, or competitive differentiation—leaving the user unable to fully explain the product or assess its value proposition.

• ›Search results consistently pointed to m-ia.app URLs (glossaire/dcf, hub/lbo, ressources) that contained real content, but all direct fetches to the domain failed with fetch errors, forcing the agent to rely entirely on search snippets and inferred content from URL titles and second-hand references.
• ›The agent inferred what M&IA does (DCF/LBO/due diligence automation, 70% time savings) from search result titles and glossary/hub URL structures, not from direct access to the homepage or feature pages—a fragile inference chain that would not have been possible without the search result titles being descriptive.
• ›Pricing, company information, and differentiation claims were entirely absent from all search results and cached content. The agent had to note these gaps explicitly rather than assemble an answer, indicating the site publishes minimal outbound-facing product information and no pricing page indexed by search.
• ›The site appears to be French-language and low-profile: it did not appear in major M&A AI tool reviews or comparison articles, and no press coverage, case studies, or founder information surfaced despite targeted searches for funding, team, and press mentions.
• ›The site's resources pages (templates, glossary definitions) were discoverable via search but not fetchable, and the main domain itself was completely inaccessible, suggesting either aggressive blocking, infrastructure issues, or misrouting of the fetch requests to a non-standard host/port.