Answer engine optimisation and generative engine optimisation for companies that need to be cited, not just ranked, as AI search takes share from traditional search.
By Citara Labs, a design and engineering studio. GCC, India, and USA.
An assessment of how the current website and content appear to AI answer engines: what is being cited, what is being ignored, and why.
The technical foundation that makes content readable and citable by AI systems. Schema markup, structured data, and the content architecture that AI answer engines can parse.
Content structured to be cited by generative AI systems: the specific format, depth, and authority signals that AI answer engines use to decide which sources to cite.
Ongoing monitoring of how the business is appearing in AI-generated answers across the major answer engines, with optimisation based on what is and is not being cited.
AI search is not replacing traditional search immediately. It is running alongside it. But the buyers who use AI answer engines are often the higher-value buyers: more informed, further along in their research, and more likely to act on what the AI tells them. Citara Labs works with businesses that need to be present in both.
Invisible in AI answers. The business has good traditional SEO but does not appear when buyers ask AI search engines about its category.
Competitor cited, you are not. AI answer engines are recommending competitors. The content and structure of the website needs to be rebuilt to compete for citations.
New market, no AI visibility. Entering a new market where AI search is how buyers discover vendors. Starting with AEO/GEO is faster than building traditional SEO authority from scratch.
Technical content, poor AI readability. The business has deep technical content that AI systems should be citing but are not. The problem is structure and schema, not content quality.
We assess how the current website appears to AI answer engines: what is being cited, what is being ignored, and what the structural and content changes are that would change that.
Schema markup, structured data, and content architecture rebuilt for AI citation. Content strategy developed for the specific answer engines the target buyers use.
Ongoing monitoring of AI citations across the major answer engines. Optimisation based on real citation data, not assumptions about what AI systems prefer.
Most AEO and GEO work is offered by content marketing agencies as an extension of their existing SEO service. Citara Labs approaches it as an engineering problem: the structure of the website, the quality of the schema markup, and the architecture of the content are the variables that determine whether an AI answer engine cites a business.
The AEO and GEO practice at Citara Labs is led by MK Singh, Sr. Partner, who has built the practice specifically around the technical and content requirements of AI answer engine citation.
Engineering approach, not content marketing. AEO and GEO at Citara Labs is an engineering discipline. Structure and schema come before content.
Led by a specialist Sr. Partner. MK Singh leads the AEO and GEO practice. This is not a general digital marketing service.
Citation monitoring included. We monitor how the business is appearing in AI-generated answers, not just how it ranks in traditional search.
Connected to platform engineering. Schema and structured data changes often require platform engineering support. That capability exists within the same studio.
What AI answer engines actually cite, and why most company websites are invisible to them.
Most company websites are invisible to AI answer engines. Not because the companies are obscure. Because the websites are structured in a way that AI systems cannot read, cite, or trust.
Read →We run our studio with a Sr. Agentic Workforce. Here is what that actually looks like.
Every company has an AI strategy. Very few have AI running inside their actual operations. This is how we run ours.
Read →AEO and GEO is one of five services under the AI Deployment practice and is led by MK Singh, Sr. Partner at Citara Labs. It is the practice most directly connected to how AI systems surface business information to buyers.
See the full practice →A short conversation with a senior partner is enough to establish what the gap is and what closing it would take.
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