Alan Wang and Albert Stanley started Serra after watching founder after founder lose precious hours to recruiting busywork. They built an AI recruiter that takes on the entire sourcing process, finding candidates across LinkedIn and GitHub, evaluating fit, and sending personalized outreach, so companies can spend their time speaking with the right people instead of endlessly searching databases.

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The idea came from conversations with dozens of YC companies caught in the same trap. Growth demanded speed, yet hiring cycles were slowed by manual sourcing that drained energy without delivering results. Recruiters spent countless hours writing Boolean strings, sifting through profiles, and chasing responses, with too little time left for real conversations with candidates who were genuinely interested. “The goal became very simple: help companies build better teams, faster,” says Stanley. “But the solution required reimagining what recruiting automation could actually achieve.”

Serra does not provide sharper search tools or better templates. It runs candidate research from end to end, evaluates fit, and manages outreach sequences on its own.

 

A hiring manager can simply say, “Find me a technical Head of Product at Series B to D startups, preferably with co-founding experience and recent promotions.” Serra clarifies the details, then searches across LinkedIn, GitHub, Crunchbase, and ATS systems until the right profiles surface.

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“Just talk or type your hiring criteria into Serra,” explains Wang. “The system clarifies details, runs exhaustive searches, vets profiles automatically, and starts personalized outreach without you lifting a finger.” Beyond keyword matching, Serra reads for context, studies career progression patterns, and writes outreach that reflects each candidate’s background. It runs continuously, keeping pipelines fresh and full without manual effort.

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The technical foundation behind Serra gives it unusual depth. Wang brings data engineering expertise from Disney Plus, where he specialized in large-scale data processing and user experience. Stanley pairs his experience as a Software Engineer at Amazon with graduate research training neural networks for genomic prediction, giving Serra machine learning capabilities far beyond typical recruiting tools.

“Albert built and trained neural networks for predicting RNA DNA hybrids in the genome,” Wang notes. “That level of pattern recognition translates directly into identifying candidate fit and analyzing career trajectories.” It is this foundation that allows Serra to map career patterns, skill development, and cultural signals that suggest alignment. The result is like having a recruiting researcher who never sleeps and never loses focus.

Serra tackles recruiting challenges on two fronts. For inbound recruiting, the system connects with existing ATS platforms to analyze application quality, identify top candidates, and enrich profiles with external data from GitHub and LinkedIn. Companies can connect their ATS and immediately identify the most qualified candidates from their inbound applications while enriching candidate data with external sources.

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For outbound recruiting, Serra proactively searches external databases to identify potential candidates who haven't applied but might be interested in opportunities. The system searches LinkedIn, Indeed, and GitHub profiles to find people who might be perfect fits but aren't actively job searching. Beyond basic automation, Serra includes sophisticated features that solve real recruiting headaches. The system detects fraudulent applications, analyzes diversity metrics, and provides insights into candidate pool composition. Companies can filter candidates, search for niche skills, and get recommended candidate fit while easily checking for fake applications and getting diversity metrics within the candidate pool to help achieve D&I goals.

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Application fraud wastes significant time during the screening process. Diversity tracking requires manual analysis that often gets skipped during busy hiring periods. Serra handles both automatically. Perhaps Serra's most game-changing innovation is continuous candidate pipeline development. Rather than conducting discrete recruiting campaigns, the system maintains ongoing searches that keep candidate pipelines fresh without manual effort.

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Companies can maintain candidate relationships over time, building networks that support future hiring needs rather than starting from scratch for each open position. "Continuously source new talent, keeping your pipeline fresh without manual effort," explains Stanley.

For growing companies, continuous sourcing creates a fundamental advantage. Instead of reactive hiring when positions become urgent, they can maintain ongoing relationships with potential candidates, enabling more strategic recruiting decisions.

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Serra represents a philosophy about how growing companies should allocate recruiting resources. Rather than building internal sourcing capacity, companies can maintain lean teams while accessing sophisticated candidate identification and engagement capabilities.

"Serra ensures your recruiting team spends their valuable time engaging and hiring the best candidates—not searching databases," notes Wang.

Recruiting teams can focus on evaluation, relationship building, and strategic hiring decisions while automation handles the time-intensive work of candidate identification and initial outreach. It's the difference between being a detective who spends all day searching for clues versus having an assistant who brings you the best leads so you can focus on solving the case.

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At its core, Serra embodies a vision where recruiting becomes a continuous, automated function rather than a periodic challenge. Companies can maintain robust talent pipelines without dedicating significant human resources to sourcing activities. "We built Serra after talking to dozens of YC founders experiencing firsthand the pain of inefficient sourcing in high-growth startups," reflects Wang.

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For founders managing rapid growth, automation could fundamentally change how they approach team scaling. Rather than choosing between slow hiring processes and expensive recruiting agencies, they can maintain high-quality candidate flow while focusing internal resources on the human elements that determine hiring success.

Serra's approach demonstrates how automation can create genuine competitive advantages by eliminating manual work rather than simply optimizing existing processes. Their vision suggests a future where the best companies win talent through superior relationship building rather than superior database searching, allowing founders to focus on what they do best while technology handles the repetitive work that used to consume so much of their time.