Companies these days deal with thousands of online résumés while job seekers send out applications into the void. Recruiters suffer from disconnects regarding what hiring managers are looking for, and candidates rarely know whether their experience properly applies to the job’s needs. The result is inefficiency on both sides: organizations struggle to find the right fit for the job, and talented professionals feel overlooked.
Carol Xie wants to change that. As co-founder of Brix, she leads a startup that’s reimagining how companies connect with talent. Instead of filtering résumés by keywords, Brix’s AI recruiter is built to more thoroughly understand the actual business problem behind a role and identify the people best equipped to solve it, turning hiring into a data-informed partnership between people and machines.
Realizing The Human Factor In Business
Having lived in the UK, Singapore, and Canada before adulthood, Carol developed an instinct early on for how people in different markets build and measure success.
While studying at the University of Toronto, she took on her first entrepreneurial endeavor as a founding member of EasyGroup, a tutoring business that, under her leadership, grew to hundreds of staff and millions in annual revenue. Managing large teams and multiple product lines while still a student gave her a firsthand look at how motivation and trust were key aspects to drive deeper business results.
After graduation, Carol shifted her focus to finance. Working at companies like TD Bank, she began to look into a recurring pattern: projects didn’t fail because the numbers were wrong but because the teams behind them were misaligned.
“Over time, I realized many business problems are actually people problems,” she said. That insight became the throughline of her career — and ultimately, the foundation for Brix.
How Brix Automates Talent Search
Recruiting, Carol observed, had remained one of the last untouched corners of business. Even as industries automated, the hiring process stayed stubbornly manual. Companies still posted job descriptions, candidates still sent identical résumés, and recruiters still scanned for the same buzzwords. “Companies say they can’t find good candidates, and candidates say they can’t find good jobs,” she explains.
Traditional recruiting, she realized, was failing to grasp what a role was actually meant to solve. “Hiring is really about solving a business problem,” she explained. “Does this person actually solve the problem?” Without that context, decisions came down to surface-level signals (like titles or keywords) rather than capability.
Brix was built to close that gap.
Carol and her team began by analyzing the headhunting process to find where context got lost. They realized that the issue came down to the difficulties that come with interpreting the relevance someone’s expertise and experience have regarding individual roles, an important inference link between what a company needs and what a person has done.
So they designed an AI recruiter that aims to choose candidates with stronger internal criteria.
It does this by analyzing a company’s job or role description using advanced language models to extract intent and key performance indicators (like desired outcomes or performance metrics). Its AI pipeline then combines that unstructured input with verified external datasets that look for candidates all around the world, translating the company’s goals into measurable and context-aware attributes like required technical knowledge or collaboration style.
From there, a ranking system maps those attributes across a global candidate graph to generate a pre-screened shortlist, with the strongest candidates ranked by relevance and organizational fit.
For Carol, the goal was never to replace the human criteria that recruiters have, but instead to give them a kind of cognitive partner. “An AI recruiter can understand domain context, like neuroscience, and evaluate whether a candidate’s work reflects the right expertise,” she said. By automating sourcing, interviewing, shortlisting, and keeping humans in the loop each step of the way, Brix aims to turn recruiting into an intelligent, evidence-driven process.
Proving The Market For Smarter Hiring
In early 2025, Brix joined the HF0 accelerator, a move that put its young team at the center of San Francisco’s competitive startup environment. Three months later, Carol and her team had built a working prototype capable of running end-to-end recruiting cycles: finding candidates, sourcing contact information, conducting interviews, and supporting offer management.
Shortly, other startups in the same cohort began using the platform, with more than half of the companies in that batch relying on it to hire founding engineers, researchers, and customer success talent — clear validation that the product had struck a serious market need.
Behind that speed was Carol’s focus on learning as the project developed. “I wasn’t technical; I had never written code,” she said. “With AI, I learned Python and can now prototype and work across front-end and back-end.” Her leadership approach mirrors that self-taught ethos. Rather than managing, she focuses on building a team culture that prioritizes autonomy and rapid iteration. “In a small startup, one smart, motivated, fast-learning person can do the work of a 10-person team,” she noted.
The platform itself reflects that philosophy. Brix focuses on evidence of proper work (like a candidate’s projects, publications, or coding repositories) far more than rigid credentials, all with the intent of broadening access to talent that might otherwise be overlooked and, in the process, redefining how recruiters can identify a “qualified” candidate.
A More Human Future of Work
With Brix now active, Carol’s end goal is to make the company a stepping stone into rethinking how organizations evolve. “We believe organizations will change a lot as people and work change,” she said. The company’s next chapter expands beyond hiring into organizational design: using AI to help teams operate more intelligently over time. Instead of static job descriptions, future systems would learn continuously from how success is measured and what it looks like inside each business.
That vision places Brix at the frontier of what she calls “contextual AI” — technology that understands nuance and intent rather than simply executing commands.
By interpreting instead of analyzing and working alongside recruiters, Carol Xie’s startup Brix aims to point to a future where hiring becomes less about filling roles and more about using technology to build relationships between employers and employees that work in the long term.
