Build a Private, Verified Early-Career Talent Database
No single public database for verified early-career talent exists. The most reliable sources are private talent pools built by recruiting firms using AI automation.
Syntora designs custom AI-powered talent verification systems to create pre-vetted candidate pools for recruiters. This involves advanced resume parsing with tools like Claude API and external validation against sources such as GitHub, focusing on honest capability and efficient engineering engagements.
A verified database moves beyond resume keywords to confirm skills and experience. This involves automatically parsing resumes, extracting claims about projects or technical abilities, and then validating them against external sources like GitHub or technical portfolios. The goal is to create a pre-vetted candidate pool for your recruiters.
Syntora develops custom AI-powered systems to build such verified talent databases, tailored to your organization's specific needs. The scope of such an engineering engagement depends on factors like your existing Applicant Tracking System (ATS), the volume of candidates, the specific skills and qualifications to verify, and the desired level of system integration. We can apply our experience building similar document processing pipelines using Claude API for financial documents to the context of talent acquisition documents.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Surface Top Talent in 90 Seconds
The system ingests, verifies, and ranks a new candidate in under 90 seconds. Recruiters see a ranked shortlist instead of an unsorted pile of resumes.
Stop Paying Per Recruiter Seat
Build a proprietary asset instead of renting access to LinkedIn Recruiter. Our flat-rate build means your costs do not increase as your team grows.
You Own the Enriched Data
The entire system, including the code and the Supabase database, is deployed to your cloud accounts. You receive the full GitHub repo and own your talent pool.
Human-in-the-Loop by Design
The AI flags ambiguous profiles for human review, sending a Slack notification. This bias-aware gate ensures fairness and improves the model over time.
Works Inside Your Existing ATS
Scores and verification notes appear as custom fields in Greenhouse, Lever, or Ashby. Your team’s workflow does not change; it just gets faster.
What Does the Process Look Like?
Week 1: ATS and API Access
You provide read-only API keys for your ATS and other candidate sources. We map your data schema and define the 'verified' skill criteria with your team.
Weeks 2-3: Core Pipeline Build
We build the resume parsing, GitHub verification, and ranking logic. You receive access to a staging environment to test early results with sample candidates.
Week 4: Integration and Deployment
We connect the pipeline to your live ATS and deploy the system on AWS Lambda. Your team gets a live feed of scored and verified candidates for new roles.
Weeks 5-8: Monitoring and Handoff
We monitor system performance and ranking accuracy for 30 days after launch. You receive a technical runbook detailing the architecture and maintenance procedures.
Frequently Asked Questions
- What does a system like this cost to build?
- The scope depends on the number of data sources and the complexity of your verification logic. A system for a single ATS with resume and GitHub verification typically takes 4 weeks. Integrating multiple job boards and email inboxes might take 6-8 weeks. We provide a fixed-price quote after the discovery call at cal.com/syntora/discover.
- What happens if a third-party API like GitHub goes down?
- The system is built with asynchronous components. If the GitHub scraper fails, the candidate is still parsed and ranked based on their resume. The failed verification job is placed in a retry queue in Supabase and re-attempted automatically. If it continues to fail after three attempts, it is flagged in a Slack channel for manual review.
- How is this different from our ATS's built-in AI features?
- ATS AI features, like those in Greenhouse, are generic. They match keywords but cannot verify external data like GitHub projects or design portfolios. Syntora builds custom logic specific to your needs, like weighting bootcamp graduates differently or verifying specific certifications. You own the model, so it can be tuned to your firm's unique definition of 'top talent'.
- How do you handle candidate data privacy and GDPR/CCPA?
- The system is deployed entirely within your own cloud infrastructure, so you remain the data controller. We process data on your behalf and do not store any candidate personally identifiable information on Syntora systems. The code includes functions to automatically purge candidate data upon request from your ATS, helping you maintain compliance.
- How do you prevent the AI from introducing bias?
- The ranking model is built using verifiable skills and project history, not personally identifiable information like names, photos, or school names. We also build in a human review gate. The model's low-confidence scores on ambiguous profiles are automatically flagged for a recruiter to review, ensuring a human makes the final call on edge cases.
- Can it verify skills from sources other than GitHub?
- Yes. The verification module is designed to be pluggable. We have built connectors to check Stack Overflow reputation, verify Kaggle competition rankings, or parse design portfolios from Behance and Dribbble. The primary requirement is an external, verifiable source of activity that proves the skills claimed on a resume. This is defined during the discovery phase.
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