How is a company GPT different from a standard chatbot?
A company GPT works on approved enterprise data, clear roles, and documented answer paths. A standard chatbot often stays generic and hard to govern.
The FAQ answers the most common questions about company GPT, AI compliance, pricing, data foundation, and the starting process in an openly readable way without interaction lock-in.
The FAQ stays HTML-first and is only optionally condensed through interaction.

Every answer has to answer, link, or qualify. At best, it does all three at once.
The FAQ stays thematically grouped so buyers, search engines, and LLM systems read the same structure.
Important answers stay visible server-side. An accordion may only be a convenience layer.
Every answer leads into a relevant deep page or the qualifying demo path.
Each group answers a recurring friction point and leads directly into the relevant deep page.

The FAQ also gets a calm visual context so the page does not feel like a naked answer block but like a curated part of the same platform.
The image does not replace content. It gives the open answer page visual calm and orientation.
Trust, regulation, and infrastructure as a reviewable operating frame instead of hero copy.
2 answers
Jump to groupExplain packages, credits, and the service layer in a way that enables self-qualification.
2 answers
Jump to groupQualify demo, first conversation, and pilot logically instead of losing visitors in a form.
2 answers
Jump to groupDefinition, differentiation, and starting logic for the central buyer query.
A company GPT works on approved enterprise data, clear roles, and documented answer paths. A standard chatbot often stays generic and hard to govern.
As soon as a clear use case, reliable enterprise data, and a traceable approval path exist or can be established. PRUVAGE answers exactly these starting questions early across data-ready, trust, and demo paths.
Trust, regulation, and infrastructure as a reviewable operating frame instead of hero copy.
Not a label, but a documented state made up of roles, evidence, data paths, and review gates. The website, publishing, and trust page have to carry the same logic.
Through documented operating decisions, owners, logging, and clearly named infrastructure paths, not through isolated hero claims. That is why trust, blog, and FAQ cross-link to the same evidence.
What has to be clarified before company GPT and productive use cases.
No. Data Ready is buying-adjacent because relevance, ownership, permissions, and later value are decided there. Without these decisions, AI stays expensive and generic.
Explain packages, credits, and the service layer in a way that enables self-qualification.
Credits make usage comparable without mixing product and services. That keeps pricing transparent while still flexible for real usage patterns.
Qualify demo, first conversation, and pilot logically instead of losing visitors in a form.
PRUVAGE clarifies context, target picture, data foundation, and the most likely starting point. The goal is a cleanly qualified next step, not a premature project sale.
Deeper answers and next steps stay directly reachable.
Open pathDeeper answers and next steps stay directly reachable.
Open pathDeeper answers and next steps stay directly reachable.
Open pathDeeper answers and next steps stay directly reachable.
Open pathOnce the key questions are clarified, trust, pricing, and demo have to be reachable without detours.