How to Uncover Top Talent with AI Semantic Search

IN THIS GUIDE

• The problem with traditional keyword searches
• What is Semantic Search?
• The differentiator: Combining Filters with Semantic Criteria
• Taking control: Required, Preferred, and Must Not
• Understanding your candidate Recommendation Score
• Best practices for AI advanced search

Your recruitment team knows the struggle of the traditional database search. To find the right candidate, you often have to build massive, complex Boolean strings just to account for every possible variation of a job title or skill. If you search for "UX Designer," you might completely miss the perfect candidate who listed themselves as an "Interaction Designer" or "Product Designer."

The last thing you want is to miss out on top-tier talent simply because they described their experience a little differently than your search query.

With Manatal’s AI Advanced Search, you can glide through the sourcing process. By utilizing intelligent Semantic Search, the system understands the meaning and context behind your query, not just the exact words.

This enhanced tool gives you greater flexibility and control, automatically assigning a relevance score to each candidate based on how closely they match your ideal profile. You can instantly zero in on the most suitable profiles without sifting through endless, irrelevant results.

Here is how recruiters are using AI Advanced Search to build better pipelines.

What is Semantic Search?

Traditional search engines rely on exact keyword matching. Semantic Search uses the latest AI-powered contextual matching (LLM) to identify candidates with related experiences, even if the wording isn't an exact match.

Instead of forcing you to guess every synonym a candidate might use, the AI does the heavy lifting for you.

• Example: If you input the Semantic Criteria for the Job Title "Frontend Developer", the AI will also surface highly relevant candidates who use titles like "UI Engineer", "JavaScript Developer", or "Web Developer".

You can apply Semantic Criteria to Skills, Job Titles, Industries, Work Experience Types (e.g., Start-Up vs. Corporate), and Education/Specialization.

The Differentiator: Combining Filters with Semantic Criteria

The true power of Manatal's AI Advanced Search is that it doesn't force you to choose between strict rules and flexible AI. You can combine both to conduct targeted, precise searches.

• Filters (The strict rules): Allow for precise, rule-based searches. Use these when you have non-negotiable, exact requirements. For example, if the candidate must be located in "New York," or must have a salary expectation "Less than $180,000," use a Filter.
• Semantic Criteria (The flexible context): Use this to explore related profiles and uncover hidden talent.

The Winning Formula: By combining a strict filter (Location = Paris) with flexible semantic criteria (Job Title = UX Designer), you ensure you only see local candidates, but you won't miss out on the local "Interaction Designer" who is perfectly qualified for the role.

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Taking Control: Required, Preferred, and Must Not

Not all search criteria are created equal. When refining your search, Manatal allows you to prioritize or exclude specific attributes by marking them with color-coded tags. This tailors the AI's results to your exact priorities.

• Required (Blue): The absolute must-haves. If a candidate doesn't meet this condition, they are excluded from the results entirely. (e.g., Required Skill: Campaign Management).
• Preferred (Green): The nice-to-haves. Candidates who match preferred criteria will receive a much higher relevance score, pushing them to the top of your list. Those who don't match will still appear lower in the results. (e.g., Preferred Skill: SEO).
• Must Not (Orange): The dealbreakers. Anyone matching this criterion will be completely filtered out. (e.g., Must Not Job Title: Intern).

Understanding Your Candidate Recommendation Score

Instead of delivering a random list of hundreds of candidates, AI Advanced Search evaluates your key criteria and combines them into one overall Recommendation Score

This percentage score reflects the exact match between the candidate and your specific search query. Each candidate's profile highlights the criteria that matched, making it easy to identify why they were surfaced and allowing you to spot the most relevant profiles at a glance.

Best Practices for AI Advanced Search

To get the most out of Semantic Search, follow these B2B sourcing best practices:

• Start broad, then refine: Begin with general terms in your Semantic Criteria. The AI will interpret the meaning and return related profiles. If you get too many results, layer in strict Filters (like Location or Years of Experience) to narrow it down.
• Don't overcomplicate it: If you aren't getting the ideal results, try simplifying your inputs or using broader synonyms. The AI understands context, so you don't need to manually input 20 different variations of a single skill.
• Leverage cross-language queries: Manatal's AI Advanced Search supports dozens of languages globally. You can search in your native language and the AI will contextually match candidates who may have written their profiles in another supported language.

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