InferenceCloud is optimized to create content that gives superior search results.

This guide describes our methodology and how this differs from traditional search optimization techniques by using new AI based approaches.

It may be worth familiarizing yourself with this guide to help get the most from search optimization with InferenceCloud.

Click on the chart to get a very brief explanation, or read on for a full explainer as well as highlights of new search developments in our pipeline.

A very brief explainer

How modern search engines work

Technology has come a long way since the the foundation of keyword based optimization techniques.

Search engines such as Google use vector space models to determine semantic meaning so that they can accurately match search queries to content using context and nuance.

However, vector spaces are complex multi-dimensional mathematical models which are hard to represent in simple terms.

For this reason people still rely on keywords as proxies to represent the best relationship between a search query and the ideal content match.

Keywords can be considered as a bottom up representation of search content.

Vector space models are essentially similar to the conversation graph that underpins InferenceCloud and its recommendations. We map specific topic areas that most resonate with your audience and your communications objective.

InferenceCloud is vector native

With InferenceCloud, your content is crafted to align perfectly with what your audience is searching for, leading to higher visibility and engagement.

Our native approach to search optimization means less reliance on keywords

1. A Topic as identified by InferenceCloud is essentially a representation of the vector that we have identified within our conversational graph.

   Or: a summarization of the best search content and meaning for your objective and audience.

   InferenceCloud topics can be considered as a top down representation of search content.

1. After identifying topics we estimate the volume of search traffic for keywords related to each topic to help you choose which topics will give you the best return on investment.

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COMING SOON

InferenceCloud will provide you with a detailed list of identified keywords for each topic to help you further optimize, or select keywords based on CPC effectiveness.

This approach helps you optimize cost for paid search by finding similarly effective (but more cost effective) keywords, and create organic content that maps to popular paid searches.

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Creating results

1. When InferenceCloud generates content we use this data to optimize for search on 2 levels

   1. Making sure that the overall semantic meaning and context of the content have the best vector match for your specific objective (or search term)

      This is the most critical element for superior search results.

   2. Specifically identifying keywords which have a particularly high visibility for use in titles or headings

      For instance, if your objective is to create content on 'best running shoes for marathons,' InferenceCloud ensures your content not only includes those keywords but also addresses related topics like long-distance running tips and shoe durability.

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NOTE

The breadth of topics you select for content generation will determine if your content is broadly applicable, or more focused to a very specific search need

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NOTE

Ensure you have sufficient insights (talking points) to cover the keyword areas that are important to you. Talking points are essential to the quality of content.

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COMING SOON

InferenceCloud will allow you to select specific search queries to optimize for and to fine tune the search keywords which are embedded

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While keywords are still part of the equation, InferenceCloud goes beyond by ensuring your content matches the intent behind the search, not just the words.

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Whilst our approach produces superior search results by creating close matching content, InferenceCloud content may appear to perform less optimally in keyword based search benchmarking tools that rely purely on keyword rather than vector signals

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