Build a Private, Verified Early-Career Talent Database

Most recruiting firms rely on a combination of LinkedIn Recruiter and their Applicant Tracking System (ATS). LinkedIn is a search engine, not a verified database. A candidate profile claiming "Python expert" could mean anything from completing one online tutorial to maintaining a popular open-source library. Recruiters waste hours sifting through self-reported skills that lack evidence.

University career portals like Handshake are essentially static resume dumps. A student uploads a PDF once and rarely updates it, meaning the data is often stale and missing context like personal projects or internship performance. Your ATS, whether it's Greenhouse or Lever, can search these resumes for keywords but cannot distinguish between academic exposure and real-world application. It can find every resume that mentions "SQL", but it cannot find the three candidates who actually used it to manage a production database.

This leads to a common failure scenario for a firm placing new grads. A client needs a Junior Data Analyst. The recruiter searches their ATS and gets 800 candidates with "Python" on their resume. They spend two full days manually reviewing profiles, only to discover that 90% have only listed it as a course they took. The workflow breaks because keyword matching cannot capture skill depth or verifiable experience.

Syntora's approach would begin with a discovery phase to understand your existing candidate sources and ATS integrations, such as Greenhouse or Lever. We would then design and implement a data ingestion pipeline using AWS Lambda, configured to trigger automatically whenever a new applicant is added.

Instead of basic keyword matching, the proposed system would utilize the Claude API to parse unstructured resume text. This advanced parsing capability extracts a detailed set of data points, including programming languages, frameworks, years of experience, project details, and GitHub profile URLs. Syntora has extensive experience using Claude API for complex information extraction from diverse document types, directly applicable to resume analysis.

For candidates with GitHub profiles, a Python script would analyze their public repositories. This analysis measures commit frequency, language distribution, and documentation quality to generate a 'project activity' score, running asynchronously to efficiently process high volumes. The system would store these new, enriched candidate profiles in a Supabase database, configured with the pg_vector extension, clearly separating raw resume claims from verified activity.

The core of the system for candidate-job matching would be a FastAPI service. When a new role is opened, this service would perform a vector similarity search within Supabase to identify candidates whose verified skills align with the job description's requirements. This process would yield a ranked list of relevant candidates, weighted by factors like skill relevance, project activity, and graduation date.

The ranked candidate list would be integrated directly back into your ATS, appearing in a custom field for your recruiters. Syntora would also develop a dashboard, potentially hosted on Vercel, to provide a detailed view of the enriched candidate profile, including the GitHub analysis and the specific resume lines that informed the match. Deliverables for an engagement of this nature typically include the deployed system infrastructure, all source code, technical documentation, and knowledge transfer to your team. A typical build timeline for a system of this complexity is generally 12-16 weeks, requiring access to your ATS APIs and collaborative input from your recruiting and IT departments. Infrastructure hosting costs for a system utilizing services like AWS and Vercel are typically modest, often in the low hundreds of dollars per month depending on data volume.