A growing number of buyers now ask an AI assistant what a company does before they ever visit its website. If that assistant describes the company inaccurately — or doesn't mention it at all — the company becomes effectively invisible to a meaningful share of its potential customers, often without any way of knowing it's happening.
This is the core problem behind a discipline now commonly referred to as AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization): making a company's information accurate, consistent, and easy for AI systems to retrieve and summarize correctly.
At SoftWin, we've worked through this problem directly. Here's what consistently produced the most reliable results.
1. Resolve inconsistent information across the web
A company's website, professional profiles, and directory listings frequently describe the same company slightly differently — different founding dates, different framing of the core offering, different terminology. Language models cross-reference multiple sources when forming an answer, and inconsistency typically results in the model guessing, defaulting to whichever version appears most often, or omitting the company altogether.
Action item: audit every public-facing profile and align the core facts (what you do, who you serve, when you started).
2. Rewrite key pages in plain, declarative language
AI models extract discrete facts rather than interpret brand narrative. A direct sentence such as "we provide X for Y kind of customer" is far more likely to be captured and reproduced accurately than abstract positioning language.
Action item: find your most abstract marketing copy and rewrite it as a single, literal sentence.
3. Structure content around questions, using proper structured markup
FAQ sections formatted with clear structured data align closely with how AI systems retrieve and extract information. A clear question-and-answer structure gives these models an unambiguous, directly citable source.
Action item: implement FAQ schema on your key pages if you haven't already.
4. Prioritize mentions on sources AI models trust more heavily
Not all online mentions carry equal weight in how these systems form their answers. Industry review platforms, respected publications, and active, well-regarded professional communities tend to influence AI-generated descriptions more than on-site content alone.
Action item: identify two or three high-trust sources in your industry and pursue genuine mentions there.
5. Establish a regular monitoring process
Rather than assuming public-facing information is accurate, companies should periodically query AI assistants directly — ideally on a quarterly basis — and treat any inaccuracies with the same urgency as an error on their own website.
Action item: set a recurring quarterly reminder to check what AI assistants currently say about your company.
Why this matters now
As AI-driven research becomes a standard part of the buying process, ensuring accurate representation across these platforms is no longer optional. Companies that treat this as an ongoing discipline, rather than a one-time fix, are better positioned to be found — and described correctly — by the growing number of customers who ask an AI before they ever ask a search engine.
Top comments (0)