Strategies, analysis, and perspectives on AI discoverability, GEO, and the future of how brands get found online.
Showing 8 of 8 posts across all categories
The biggest challenge for e-commerce websites is keeping the load times for heavy product images and real time data to the minimal. High resolution images help sell products better, however, if loading of HD images first take up a lot of time, AI agents will not able to get to the product data at all. Similarly, real time stock checks are important but we cannot run too many javascrips. Heavy load times kill generative engine optimisation and hence AI visibility of our product altogether.
LLMs use direct inputs like system generated meta data (GPs location, date, time etc), context from chat memory (user profiling via past conversations), linguistic inference and tool calling techniques to decipher you location and understand the locational context of your inout query.
Zero-party data is the information shared by the customer intentionally through interviews, surveys, quizzes, testimonials etc. This zero-party data when used strategically has the potential of becoming a core marketing asset. Let’s learn how.
Discoverability on the web essentially means optimising your website and content to be parsable, readable, citable by AI engines and bots. There was a time when we used to block bots on our website, now we have to enable them, securely, maintaining privacy of the user data and dealing with all the security loop holes which any malicious actor can exploit.
In present day world of zero click searches and answer engines, share of model is becoming a critical strategic metric. Share of model is the percentage of times an AI answer engines like Gemini or ChatGPT mentions, cites or recommends your brand compared to your competitors for a certain query set.
Natural language processing is the linguistics field that is responsible for interactions between computers and humans. NLP helps machines understand, interpret and generate human understandable language. NLP has enabled the large language models and answer engines based on them. Hence, understanding NLP is quite crucial to understand the discoverability on the web and build AI visibility.
Knowledge graph is a concept which has been used by google to create this massive database of facts, entities and their relationship with each other. It is not about random strings of character. The objective of building knowledge graphs is for the machines to understand the world how humans do.
We see a big shift from SEO to GEO for businesses which have an online presence. For decades now SEO has been the bedrock of visibility on the web. However, with LLM based answer engines we have entered a world where models like chatGPT, Gemini, Claude or perplexity synthesise information directly for the user. This big shift in the nature of how users get their information on the web has led to the emergence of the discipline of generative engine optimisation (GEO).