How I Built and Launched a KYC Selfie Verification API in...

I've always wanted to build a product I could sell online.
Last week I did it — a full AI-powered KYC API, live in production,
listed on RapidAPI, with a registration system and usage dashboard.
Here's exactly how I built it.

What is KYC?

KYC (Know Your Customer) is the process of verifying a user's identity.
Every fintech, bank, marketplace, and crypto platform needs it.
Most solutions cost thousands of dollars per month. I built one for free.

What the API does

✅ Face detection and centering check
✅ Liveness detection (real person vs photo of photo)
✅ Age estimation (18+/21+ flags)
✅ Glasses and mask detection
✅ Emotion detection
✅ Background quality check
✅ ID document data extraction (name, DOB, document number)
✅ Selfie vs ID document face matching
✅ Confidence score 0-100

The tech stack

FastAPI — Python REST API
Gemini Vision via OpenRouter — AI image analysis (free tier)
SQLite + SQLAlchemy — database for storing verifications
Railway — deployment (free tier)

The API in action

Get a free API key at:
https://kyc-selfie-api-production.up.railway.app/register

Then verify a selfie in Python:

import requests

response = requests.post(
    "https://kyc-selfie-api-production.up.railway.app/v1/verify/selfie",
    headers={"x-api-key": "your-api-key"},
    files={"selfie": open("photo.jpg", "rb")}
)

result = response.json()
print(result)

Response:

{
  "approved": true,
  "confidence": 92,
  "face_detected": true,
  "face_centered": true,
  "eyes_open": true,
  "liveness_score": 95,
  "age_estimate": 26,
  "appears_18_plus": true,
  "glasses_detected": false,
  "mask_detected": false,
  "emotion": "neutral",
  "background_quality": "clean",
  "lighting_quality": "good",
  "issues": []
}

Extract data from an ID document

response = requests.post(
    "https://kyc-selfie-api-production.up.railway.app/v1/verify/document",
    headers={"x-api-key": "your-api-key"},
    files={"document": open("id_card.jpg", "rb")}
)

doc = response.json()
print(f"Name: {doc['full_name']}")
print(f"DOB: {doc['date_of_birth']}")
print(f"Number: {doc['document_number']}")

Full KYC — match selfie against ID

with open("selfie.jpg", "rb") as s, open("id.jpg", "rb") as d:
    response = requests.post(
        "https://kyc-selfie-api-production.up.railway.app/v1/verify/kyc",
        headers={"x-api-key": "your-api-key"},
        files={"selfie": s, "document": d}
    )

result = response.json()
print(f"Match approved: {result['approved']}")
print(f"Confidence: {result['confidence']}%")

Available endpoints

Endpoint	Description
`POST /v1/verify/selfie`	Selfie analysis
`POST /v1/verify/kyc`	Selfie + ID matching
`POST /v1/verify/document`	Extract ID data
`POST /v1/verify/age`	Age estimation
`GET /v1/status/{id}`	Check past result
`GET /v1/usage`	Usage stats

Pricing

Plan	Price	Calls/month
Basic	Free	100
Pro	$49/mo	10,000
Ultra	$99/mo	50,000

What I learned

Building this in one day taught me a few things:

1. AI APIs make everything easier. Instead of training a custom face
detection model, I used Gemini Vision via a simple API call. The prompt
engineering took 10 minutes.

2. FastAPI is incredible. Auto-generated docs at /docs,
type validation, async support — everything just works.

3. Distribution is harder than building. The API took one day.
Getting people to use it is the real challenge.

What's next

Arabic and Moroccan CIN support (huge untapped niche)
Email notifications when verification completes
React SDK for easy frontend integration
Video liveness detection

Try it free

🔗 Register: https://kyc-selfie-api-production.up.railway.app/register
🔗 Live demo: https://kyc-selfie-api-production.up.railway.app
🔗 API docs: https://kyc-selfie-api-production.up.railway.app/docs
🔗 RapidAPI: https://rapidapi.com/balirh12/api/kyc-selfie-verification

Free plan — 100 verifications/month, no credit card required.

Built with ❤️ using FastAPI, Gemini Vision, and Railway.
Questions? Drop them in the comments!

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