Intelligent Guided-Search Engine

Our guided-search engine offers a new type of search experience by enabling step-by-step guidance within the search process. While other technologies extrapolate information from textual content, our engine extracts meaningful information from the search queries directly. Through this meta-search layer, the engine enables a step-by-step interactive visual dialog to continually narrow down and re-evaluate results, and high-level analytics based on what users are searching for.
 

Why is it better?

Terminology:
 

With a standard search engine, user's keywords are compared with the textual content of documents. While users might refer to the same concept in many different ways, the content of documents may as well vary greatly, making search a very frustrating trial and error process for the user.

With our guided-search engine, the different terminologies for a common concept are grouped together and then associated to documents in a unified way.

Under-define / Over-define:
 

With a standard search engine, user's constantly face situations from under-defining their query (producing too many results) to over-defining their query (producing no results at all). This results in a highly frustrating trial and error process as it is not possible to know what keyword to add or to change to reach an adequate list of results.

With our guided-search engine, the automated and interactive step-by-step visual dialog will adequately suggest to the user concepts to add or to remove from the query according to the content of the database.

Improving data:
 

In a standard search engine, it is not possible to request an analytic report of what types of information are lacking in your database as requested by users. Since keywords are directly matched the absence of a high-level analytic report impedes your organization from building a truly customer-centric knowledge base.

With our guided-search engine, missing concepts and unfulfilled filters are easy to identify. Defining what types of information lacking in your knowledge base to better service your customers is simply an extension of the way our engine performs.

Self-learning:
 

With a standard search engine, the self-learning capacities are greatly limited by the variables of the environment. Even if usage can help fine-tune the ranking and matching accuracy, it is very limited and doesn’t improve the overall user experience.

With our guided-search engine, self-learning is well contained within our filtering environment and allows for much higher levels of learning (vocabulary expansion, granular popularity ranking and association correction) having a direct impact on the quality of the user experience.

How does it work?

The engine extracts meta-associations from the user's search input to automatically set the active filters. Based on this filter definition, the engine generates in real time the most efficient questions (which will have the most significant filtering effect on the current list of results) to ask the user. Through this active filtering process the user will very quickly and intuitively reach a single result precisely matching their request. In the case where the result doesn't correspond to the user’s expectation the engine will help re-evaluate the active filters and then trigger a diagnostic report transfer for resolution.