Beyond the Match Score: How Zeigi Finds Your True Fit

You’ve seen it before. A notification pops up, glowing with promise: “We’ve found a 95% match for your profile!” You click, your hopes high—only to find a role that’s completely irrelevant. Different seniority, unrelated industry, or worse: just a few random keywords in common with your resume.

So what does that “match score” really mean? Too often, it’s a shallow calculation built on an outdated method: keyword counting.

At Zeigi, we think you deserve better. That’s why we built a platform that goes beyond superficial scores to capture the semantic essence of your career and the opportunities around you.

The Problem With Keyword Match Scores

Most job-matching algorithms still use keyword frequency as their foundation. They scan resumes and job descriptions for overlaps:

• Resume says “Python.” Job says “Python.” → +10 points.

• Resume says “Agile.” Job says “Agile.” → +10 points.

Stack up the points, call it a “95% match.”

The problem? This rigid approach misses everything that matters:

• Context → Can’t distinguish between junior and senior roles.

• Synonyms → “Customer success” vs. “Client relations” look unrelated.

• Conceptual connections → It doesn’t understand that dashboards are a way of telling stories with data.

The result: irrelevant “matches” and endless frustration.

From Counting Words to Understanding Meaning

Zeigi takes a different path: semantic matching.

Imagine a detailed map of professional skills and concepts. Keyword search is like searching for an exact street address—if you’re off by a letter, you’re lost.

Semantic search is different. It’s like saying: “Show me places with the same spirit as this lakeside cabin.” The map doesn’t just find exact duplicates—it finds related cabins, forest clearings, and scenic viewpoints.

Technically, Zeigi converts every job description and profile into vector embeddings—unique coordinates on a map of meaning. A “perfect fit” isn’t about word overlap, it’s about how close your career coordinates are to a role’s coordinates.

A Concrete Example: BI Analyst vs. Data Storyteller

Let’s make this real.

You’re a Business Intelligence (BI) Analyst. Your skills include:

• SQL

• Tableau / Power BI

• Data Warehousing

• Python for analysis

• Building dashboards and reports

A traditional keyword system would miss a job called “Data Storyteller”. Why? Because the description says:

• “Transform raw data into compelling narratives.”

• “Communicate insights to non-technical stakeholders.”

• “Build presentations that tell a story.”

To a keyword matcher, there’s no overlap. To Zeigi, it’s a perfect fit.

Our semantic model knows that dashboards are a storytelling tool, SQL powers insight generation, and BI Analysts and Data Storytellers perform nearly identical functions. Zeigi connects the dots where other systems can’t.

What This Means for Your Career

With Zeigi’s semantic recruitment, you get:

• Hidden Opportunities → Discover roles with unconventional titles you’d never think to search.

• Relevant Recommendations → A curated feed of jobs that truly align with your skills.

• True Market Value → See how your skills transfer across industries and functions.

• Time Saved → No more chasing irrelevant “95% matches.”

Demand More From Your Job Search

Your career is more than a list of keywords—and you deserve a platform that understands that.

At Zeigi, we’re building a system where jobs find you, based on the real meaning of your skills and experiences.