When candidates apply for jobs, they list skills like Python, C++, SQL, or Machine Learning on their resumes.
But for recruiters, these skills are:
- Self-reported
- Unverified
- Often inflated or inaccurate
A resume saying "Proficient in Python debugging" does not prove that the candidate can actually debug Python code.
Recruiters either:
- Trust the resume blindly
- Or spend time validating skills through interviews and tests
This creates friction, bias, and inefficiency in hiring.
CredAI replaces unverifiable resume skills with AI-evaluated, cryptographically verifiable micro-credentials.
Instead of claiming a skill, candidates can prove it.
CredAI:
- Runs a skill-specific AI oral exam
- Grades answers using a transparent rubric
- Produces evidence of reasoning and correctness
- Anchors a hash of that evidence on Solana
- Issues a verifiable credential ID that recruiters can independently check
- Not a quiz — candidates must explain reasoning and debugging steps
- Skill-aware — exams adapt to the exact skill entered (free text)
- Verifiable — credentials are backed by an immutable blockchain proof
- Explainable — recruiters see scores, verdicts, and transcript evidence
- No trust required — verification does not rely on the issuer
Example: Python debugging, C++ OOP, SQL joins
- Gemini generates a real, practical problem
- Candidate answers in text
- AI grades using a rubric (0–10)
- Adaptive follow-up questions are asked
- If inconsistent → REVIEW
- If below threshold → FAIL
- If strong → PASS
- Exam evidence is serialized into JSON
- A SHA-256 hash is computed
- Hash is written to Solana devnet via a Memo transaction
- A unique
credentialIdis issued
- Recruiter opens
/verify - Pastes
credentialId - Backend verifies:
- Credential record
- Solana transaction existence
- Matching evidence hash
- Returns Verified ✅ with explorer link
CredAI includes an admin dashboard for transparency and auditability:
- Total sessions
- Pass / Fail / Review counts
- Issued credential count
- Flagged attempts
Per-session detail view:
- Full transcript (Q/A)
- Scores and feedback
- Final verdict
- Blockchain proof links
- Next.js (App Router)
- Tailwind CSS
- Client-side routing and state
- FastAPI (Python)
- Google Gemini 2.5 Flash (exam generation + grading)
- Solana devnet (credential proof anchoring)
- SHA-256 evidence hashing
- Solana Memo program for immutable storage
| Endpoint | Method | Description |
|---|---|---|
/session/start |
POST | Start exam with { "skill": "Python debugging" } |
/session/{id}/answer |
POST | Submit answer with { "answer": "..." } |
/session/{id}/finalize |
POST | Finalize exam |
/credential/{credentialId}/verify |
GET | Verify credential |
/dashboard/metrics |
GET | Get dashboard metrics |
/dashboard/sessions |
GET | Get all sessions |
/dashboard/session/{id} |
GET | Get session details |
cd services/api_py
python -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8080cd apps/web_dashboard
npm install
npm run devOpen: http://localhost:3000
GEMINI_API_KEY=your_key_here
SOLANA_ISSUER_PRIVATE_KEY_BASE58=your_devnet_wallet_keyNEXT_PUBLIC_API_BASE=http://localhost:8080- In-memory storage (MVP)
- Solana devnet only
- No user authentication (candidate flow is open)
- Persistent storage (SQLite / Firebase)
- PDF certificates with QR verification
- Auth-protected recruiter dashboard
- Multi-attempt skill history per candidate
- On-chain credential registry
Resumes list claims.
CredAI provides proof.
By transforming skills from unverifiable text into auditable, verifiable credentials, CredAI bridges the trust gap between candidates and recruiters — and makes skill-based hiring fairer, faster, and more transparent.