Candidate searching through an efficient search engine that delivers instant results in a “smart” way is the minimum expectation of organizations today, an “intelligent” search engine that reduces the most labor-intensive tasks and delivers results almost immediately and accurately is something that has not been done.

Let’s use the context of recruitment service, which screens many resumes to search for the ideal candidate. Applications get lost, candidates become frustrated and satisfaction rates continue to drop. Candidate search is a newer concept in the industry, using an intelligent candidate search engine with feedback parameters based on previously hired performance is a large jump in performance.

Backtrack to 20 years ago, when the internet wasn’t considered a vital commodity in society, and neither was it designed to contextualize information to produce the most relevant results. There were approximately only two million computers connected to the internet, with another approximation of just 10000 websites. From the invention of the WWW by Tim Berners-Lee in 1991 as a virtual library to locate URLs for various websites, to the first Yahoo! search that indexed web pages, to Ask Jeeves, designed to attempt responses to search queries using human editors, and finally, the birth of Google. In 2003, Google’s first search algorithm update was launched. A search engine algorithm is designed to apply the relevancy of search results to a user, identified from the index to hierarchically rankings.

Fast forward to today, with the world now being run through a screen people are now relying on the use of handheld devices to access content and services, and what puts organizations under even more pressure to deliver is that consumers will only use the best-in-class services that are readily available at the time of their choosing. The keyword here is Performance, which is undeniably the most important element of strong consumer and candidate engagement.

Today’s search engines are expected to deliver on these demands but go even further. The introduction and development of artificial intelligence (AI) means search engines are not just through web browsers but are now custom-developed systems built on the back of classification algorithms and data mining.

These search engines are expected to be smart and instant, where searches produce accurate, relevant results in real-time based on things such as browsing history, popular keywords, and high-performance content from previous searches, to not only deliver instant results but for search engines to learn from user content to drive an “intelligent” experience that works for the consumer. This is what’s known as an AI-powered search, a search engine coveted and powered by artificial intelligence (AI)


Today’s branch of computing is run through artificial intelligence designed to mimic human intelligence, meaning to process, adapt and learn from data input. For example, if a user inputs a keyword search, the platform is expected to learn from those keywords and automatically generate the most relevant searches in real-time for future searches. This is the ability for AI to perform this without the need for human intervention. Gone are the days of a simple search and the return of results. Consumers expect AI-powered search engines to work for them and deliver accuracy and relevance every single time.

AI Powered Candidate Search

The fundamental idea behind this is for intelligent machines to continuously adjust its results until it becomes sharp in accuracy and relevancy and learn exactly like a human over time. The system caters for critical data points, for example, within a CV resume like location, key skills, and experience to locate the best match for a client candidate requirement. Consider the example of an AI-powered search through Pinterest, where AI learns the image-based searches that users perform, and this data is fed through a deep learning model that learns user history to understand its searching context and deliver a more personalized experience for every user. The model learns about the users searching intent to deliver the relevant results the user expects.

Let’s break down the key capabilities of an AI-powered search.

Personalized and Contextual

The ability for AI-powered search engines to take the user context, query context and context to better learn and understand the intent of the user.

  • User Context – The user context describes the activity of a user on a search engine and, therefore, analyzing search histories, previous interactions, recommendations, profiles and locations.
  • Query Context – The query context describes the search elements such as the use of keywords as part of a search and formulates the relevant results that link to it.
  • Domain Context – The underlying business rules that define the inventory and terminology upon performing a search.

In the context of ranking appropriate resumes, it contextualizes information, locates critical data points, and identifies the candidates that match a certain requirement or vacancy position.

Predictive Intelligence

AI-powered searches can analyze inputted data in a search and perform predictive text, including any spelling corrections and phrase detection, to deliver the right results and continue to become smarter. This eliminates the need for repetitive searches.

Semantic Searching

The intent of using exact words that identifies the searchers meaning through contextual queries. The use of semantic search, along with NLP allows the search to function independently and deliver the most relevant results.

Candidate Search


A report from the Korn Ferry Global survey found that 63% of respondents said artificial intelligence significantly altered the recruitment process in their organization. The findings were surveyed from 800 talent acquisition professionals, with at least two-thirds of respondents believing AI-powered candidate searches have transformed recruitment, and that there is more fairness to garner the most appropriate and quality candidates. The ability of an AI-powered engine to contextualize the data in real-time allows recruiters to focus on the quality of service rather than filtering through too much data.

At Inflection Poynt, we deliver a more sophisticated and efficient screening process that endeavors to deliver on these findings, as well as ensuring the right candidates are valuable for the long-term for a strong return on recruitment investment. The ability for AI-powered candidate searches to search and match particular skill sets, work history and behaviors helps to efficiently identify candidates that are best suited for a role.

What an AI-powered candidate search also allows for is more consideration to strengthen and nurture candidate relationships, which is a key commitment at Inflection Poynt, to excel in client service with acute attention paid to hiring the right candidate.

Our smart-sourcing marketing engine allows us to automate our headhunt for the strongest candidate through the contextualization of multiple resumes. The end goal is for the candidate data to match the exact requirement to deliver a pool of talent as part of the results, and this is performed through contextualizing the data to deliver the best compatibility results. This enables us to identify the best candidates based on the candidate information performed through keyword scanning. 

The identification of candidate searching through resumes is now instant, with the elimination of labor-intensive tasks such as searching, screening and candidate identification in exchange for higher client satisfaction rates and better identification of talent.

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