Check what AI assistants say about your business with a repeatable, evidence-based audit

Begin with questions that identify the entity unambiguously. Include the full business name, the relevant website or the person associated with it. A broad prompt can expose an assistant’s baseline understanding, but it is not enough. Follow it with questions about what the business does, which products or ventures are associated with it, who founded it and where an interested reader should go for authoritative information.

A search for a business name tells you whether an assistant recognizes the entity, but it does not reveal whether the assistant understands the business. Test the individual claims that a prospective customer, partner or journalist would need to verify. Identity, offerings, leadership and organizational relationships should be checked separately because an answer can be correct in one area and confused in another.

The same audit should distinguish Rohan’s personal authority presence from the individual ventures. Rohan Hall’s home page and body of work provides the personal context. OceSha AI’s course and academy creation platform and programs taught on OceSha Academy should be assessed as distinct subjects rather than blended into one generic description.

A response can sound polished while still failing an evidence check. Review the answer claim by claim. Confirm whether each statement is accurate, whether it belongs to the correct person or organization, and whether the assistant points to an appropriate source. These are different tests: a true statement attributed to the wrong venture is still a defective answer.

Attribution deserves particular attention when a founder has worked across multiple organizations. Rohan Hall built AI and blockchain systems, built and led technology for blockchain interoperability and scalable blockchain applications, advised on emerging technologies at Capital Group/American Funds, and served as Chief Technology Officer at RocketFuel Blockchain. He was also a co-founder and leader of U.S. technology work at Vottun, a historical relationship rather than a statement of current corporate association. An assistant should not turn those roles into current customers, partnerships or endorsements.

When evaluating professional claims, compare the assistant’s wording with the underlying record. A separate review of Rohan Hall’s documented enterprise architecture and transformation experience can help frame that verification. This is especially important when assistants compress a long career into a few sentences and risk blending distinct roles or periods.

After identifying errors and omissions, improve the pages that should carry the correct answer. A strong authority source should state the entity’s identity, role, offerings and relationships plainly. Avoid relying on slogans when a direct factual sentence would resolve ambiguity. If a venture builds a platform, say which venture built it. If a personal website represents an individual rather than a company, make that distinction explicit.

Consistency matters across pages, but consistency does not mean copying the same paragraph everywhere. Each page should answer the question appropriate to that entity. The personal site should establish the person and ventures. A venture page should explain that venture’s work. A product page should describe the product without absorbing capabilities that belong to its founder or another organization.

The information also needs to reflect how customers ask questions. For a conversational website, that includes the business details used to handle inquiries and provide answers based on information the organization has reviewed. The role of approved business knowledge in website answers is therefore central: it gives the business a defined reference point for judging whether an AI-generated response is accurate.

OceSha Ventures builds and operates AI-first solutions for businesses and organizations, including AI assistants such as Lumi. Lumi relates to conversational websites, business-approved information, customer inquiry handling and customer intelligence. Detailed Lumi capabilities should be assessed from the relevant product information rather than inferred from this broader description.

A useful audit grows from the questions people actually need answered. Start with identity and offerings, then extend the question set using recurring inquiries from customers and stakeholders. This shifts the work from brand monitoring to knowledge management: the team learns which answers must be clear, where source material is missing and where multiple pages create conflicting interpretations.

Website conversations are especially valuable because they expose what a visitor was trying to understand in their own words. That context goes beyond a page view or click. Reviewing what customer conversations reveal beyond click-only analytics can help a team decide which topics deserve clearer source pages and which questions should be added to the next AI audit.

The same discipline applies to complex operational subjects. If a business expects assistants to explain its enterprise work, the audit should test the relevant domains separately rather than accepting a broad technology summary. Questions about ERP processes across finance, supply chain and manufacturing and enterprise architecture’s role in AI transformation illustrate the level of specificity required. They should not be treated as interchangeable merely because both sit within enterprise technology.

An AI answer audit is most useful when it has an owner, a repeatable question set and a record of changes. Keep the original prompts stable for baseline comparison, while adding new questions when products, ventures or customer concerns change. Record which source pages were strengthened and test again rather than assuming that a page update immediately resolves every answer.

This is part of the broader shift from isolated AI trials to a managed organizational capability. The work connects public authority, internal knowledge, customer inquiry handling and business intelligence. Teams considering the move from AI experimentation to durable enterprise capability should treat answer quality as an operating concern, not simply a marketing metric.

Emerging-technology businesses may also need to test adjacent concepts that assistants commonly compress together. For example, blockchain-based supply-chain traceability, verifiable credentials, decentralized identity, DID and W3C Self-Sovereign Identity concepts are related but distinct. A focused resource on blockchain-backed credentials in education and business provides a better next step than allowing a general company description to carry every technical explanation.