QuestGuard

Inspiration

Hiring is based heavily on resumes and certificates, but these can sometimes contain false or incomplete information. We wanted to solve this trust problem in a simple way. Just like CIBIL helps build trust in lending, we wanted to create a Trust Score for hiring that looks at multiple professional signals instead of only checking documents.

What it does

QuestGuard analyzes a candidate's GitHub profile, resume, LinkedIn information, and certificates and generates a Trust Score from 0–100. It checks things like GitHub activity, resume timeline, skills, and certificate authenticity. It also gives the candidate practical suggestions to improve their score. The score can be stored as an EAS blockchain attestation and verified using a QR code.

How we built it

We built QuestGuard using Next.js, React, Tailwind CSS, Supabase, Python FastAPI, n8n, and blockchain technologies. n8n works as the main orchestration layer. It collects GitHub data, sends information to AI models for analysis, processes the results, and calculates the final Trust Score using weighted formulas.

Challenges we ran into

One of our main challenges was combining different types of data such as GitHub activity, resumes, and certificates into one meaningful score. We also had to design a reliable scoring system that could detect suspicious patterns while keeping the process understandable. Integrating AI, APIs, n8n workflows, blockchain attestations, and QR verification into one system was another major challenge.

Accomplishments that we're proud of

We are proud that QuestGuard goes beyond simple "Verified / Not Verified" checks. It creates a complete Trust Score and also tells candidates how they can improve. We also built blockchain-based verification and an offline QR verification concept, making the score portable and easier for employers to verify.

What we learned

We learned how to connect AI, APIs, automation, databases, and blockchain into one complete product. We also learned that building a scoring system is not only about collecting data; the data needs to be normalized, analyzed, and converted into meaningful results. Working on QuestGuard also improved our understanding of workflow automation through n8n and real-world AI integration.

What's next for QuestGuard

Our next goal is to improve the accuracy and reliability of the Trust Score, add more professional verification signals, and make the Improvement Engine more personalized. We also want to improve the employer experience so companies can quickly verify candidate profiles and understand their professional strengths. In the future, QuestGuard can become a portable professional trust profile that candidates can use across different hiring platforms.

Built With

• backend
• node.js
• python
• qrcode
• react
• typescript