How I Built and Launched a KYC Selfie Verification API in One Day (Free to Try)

#api #ai #python #webdev

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.