Vision • Language • Audio • Documents
One Platform. ISO27001 Certified. 10 Years Proven.
Boutique expertise meets enterprise-grade infrastructure
Dedicated 10-person team vs. thousands of engineers. Direct access to architects who understand your business.
ISO27001 certified infrastructure. Private cloud options. Your data never trains our models.
Cloud, hybrid, or on-premise. Custom SLAs. White-glove support for your infrastructure.
Comprehensive multimodal AI capabilities designed for enterprise needs
Advanced computer vision for real-time object detection, scene understanding, and visual intelligence.
ANPR (License Plate Recognition)
Real-time vehicle identification with 99.2% accuracy across 200+ countries. Process 50,000+ vehicles per day with sub-second response times.
Read Full Case StudySee how Smart Networks transforms business operations with multimodal AI
Challenge:
Legacy OCR system struggled with 40% accuracy on international license plates, causing payment disputes and operational delays.
Solution:
Deployed Smart Networks' Vision AI for ANPR (Automatic Number Plate Recognition) with real-time vehicle tracking across 200+ countries.
Key Results:
Challenge:
Manual quality control created bottlenecks, with 15% defect slip-through rate costing millions in recalls.
Solution:
Computer vision system for real-time defect detection on production lines with sub-second analysis.
Key Results:
Challenge:
Manual document verification for KYC compliance took 5-7 days, creating poor customer experience.
Solution:
Document Intelligence AI for automated ID verification, document extraction, and compliance checking.
Key Results:
Integrate multimodal AI into your applications with clean, well-documented APIs. Built for scale, designed for simplicity.
Python, Java, Node.js, and .NET libraries
Sub-second response times with global CDN
Direct access to solutions architects
# Smart Networks Multimodal API
from smartnet import Client
client = Client(api_key="your_enterprise_key")
# Analyze license plate (ANPR)
result = client.vision.analyze_image(
file="vehicle.jpg",
tasks=["plate_recognition", "vehicle_make"],
regions=["US", "EU", "APAC"]
)
# Process result
print(f"Plate: {result.plate_number}")
print(f"Confidence: {result.confidence}%")
print(f"Vehicle: {result.vehicle_make}")Join enterprises worldwide who trust Smart Networks for secure, scalable AI solutions
📧 enterprise@smartnet.club
🌍 Serving enterprises in 50+ countries