FOR IMMEDIATE RELEASE

Kuala Lumpur, 23 January 2026
Favoriot today announced the launch of its new Lite Plan, a lower-entry subscription designed to help students, educators, beginners, startup founders, and developers begin their IoT journey using a real, production-grade platform.

The Lite Plan addresses a growing need for a simple, affordable way to connect devices, view data, and understand IoT workflows without the complexity often found in larger, enterprise-focused plans. Users on the Lite Plan gain access to the same core Favoriot platform used by commercial and government deployments, scaled to suit learning, experimentation, and early validation.

“The Lite Plan is about removing friction at the starting line,” said Dr. Mazlan Abbas, Co-Founder and CEO of Favoriot. “Many users want to learn or test ideas with real devices and real data, but do not need advanced features yet. This plan gives them a proper foundation without overcommitment.”

Designed for Specific User Segments

The Lite Plan is best suited for:

• Students and educators working on coursework, labs, or final-year projects who need hands-on experience with an industry platform
• IoT beginners using devices such as ESP32 or Arduino and learning basic device-to-cloud data flows
• Startup teams and founders building proofs of concept or early prototypes
• Developers and technologists evaluating IoT platforms before selecting a long-term solution

Clear Progression Across Plans

Favoriot positions the Lite Plan as a starting point within its broader subscription structure:

• Lite Plan: Entry-level access for learning, testing, and early exploration
• Beginner Plan: Expanded usage for wider testing and multiple devices
• Developer Plan: Application development, integrations, and pilot deployments
• Professional and Enterprise Plans: Full-scale production, security controls, and operational workflows

Users can upgrade plans as their projects grow, with continuity across devices and data.

Availability

The Favoriot Lite Plan is available immediately. Full pricing and plan details can be found at:
https://www.favoriot.com/iotplatform/pricing/

About Favoriot
Favoriot is a Malaysia-based IoT and AIoT platform provider supporting smart city, agriculture, manufacturing, education, and enterprise use cases. The platform enables secure device management, real-time data ingestion, analytics, and automation for organisations at every stage of adoption.

Media Contact
Favoriot Communications Team
https://www.favoriot.com

This guide will show you how to build an AI-powered quality inspection system using a camera and send inspection results to the FAVORIOT IoT platform in real time.

🔧 Step 1: What You Need

Hardware:

• Raspberry Pi (or any computer with a camera)
• Camera (USB or Pi Camera)
• Internet connection

Software:

• Python 3
• Libraries: opencv-python, tensorflow, numpy, requests

🛠️ Step 2: Install the Required Software

Open Terminal and run:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip -y
pip3 install opencv-python numpy requests tensorflow

🧠 Step 3: Train an AI Model to Detect Defects

Create a folder called dataset_defects with 2 subfolders: defect and normal.

Now, use this Python code to train the model:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

dataset_path = "dataset_defects"
batch_size = 32
img_size = (224, 224)

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="training"
)

val_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="validation"
)

base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights="imagenet")
base_model.trainable = False

model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(train_data, validation_data=val_data, epochs=10)

model.save("defect_detection_model.h5")

🎥 Step 4: Real-Time Defect Detection Using Camera

Once the model is trained and saved, run this script:

import cv2
import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model("defect_detection_model.h5")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    img = cv2.resize(frame, (224, 224))
    img = np.expand_dims(img, axis=0) / 255.0
    prediction = model.predict(img)[0][0]

    label = "Defect Detected!" if prediction > 0.5 else "Product OK"
    color = (0, 0, 255) if prediction > 0.5 else (0, 255, 0)

    cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
    cv2.imshow("Quality Inspection", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

🌐 Step 5: Send Defect Results to FAVORIOT

✅ 1. Set Up a Device in FAVORIOT

1. Log in to Favoriot Platform
2. Go to Devices → Add Device
3. Note down your Device Developer ID and API Key

✅ 2. Add Code to Send Data

Below is the function to send results:

import requests
import json

DEVICE_ID = "YOUR_DEVICE_ID"
API_KEY = "YOUR_FAVORIOT_API_KEY"
URL = "https://apiv2.favoriot.com/v2/streams"

def send_data_to_favoriot(status):
    payload = {
        "device_developer_id": DEVICE_ID,
        "data": {
            "status": status
        }
    }

    headers = {
        "Content-Type": "application/json",
        "Apikey": API_KEY
    }

    response = requests.post(URL, data=json.dumps(payload), headers=headers)
    print("Response from Favoriot:", response.json())

✅ 3. Combine with Real-Time Detection

Add this snippet inside your prediction logic:

if prediction > 0.5:
    send_data_to_favoriot("Defect Detected!")
else:
    send_data_to_favoriot("Product OK")

📊 Step 6: View Data on FAVORIOT Dashboard

• Go to your device on the FAVORIOT Dashboard
• Click on Streams to view defect data
• You can also create graphs or alert rules for monitoring

🚀 Bonus Tips

• Add Telegram Alerts using Telegram Bot API
• Add Dashboard Charts using Favoriot’s visualization
• Improve accuracy with better dataset or model tuning

✅ Summary

With this project, you have:

✅ Built a real-time defect detection system
✅ Displayed results on screen
✅ Sent reports to FAVORIOT cloud platform

References

• Favoriot Platform Registration (FREE and Paid)
• Favoriot Full Documentation (Official)
• Favoriot Github
• How-To Use Favoriot Platform Video Playlist

Disclaimer

This article provides a step-by-step guide and only serves as a guideline. The source code may need adjustments to fit the final project design.

Puchong, Malaysia – March 5, 2025 – A game-changing partnership has been forged today as Favoriot Sdn Bhd and Aswant Solution Sdn Bhd officially signed a Memorandum of Understanding (MOU) at Favoriot’s office in Puchong, Malaysia. This strategic alliance is set to supercharge innovation in Artificial Intelligence (AI), the Internet of Things (IoT), and cybersecurity, empowering businesses with cutting-edge technology solutions.

The agreement was formalized by Dr. Mazlan Abbas, CEO of Favoriot Sdn Bhd, and Nor Asrul Mohd Noor, Managing Director of Aswant Solution Sdn Bhd, with Zura Huzali, Business Development Director of Favoriot, and Fazlirizam Mohammed Nor, Director of Aswant Solution, witnessing the milestone moment.

Powering the Future with AI-Driven IoT and Security Solutions

This collaboration is more than just a handshake—it’s a commitment to shaping the future of AI and IoT-driven security solutions. By combining Favoriot’s expertise in IoT platform development with Aswant Solutions’ prowess in IT security and distribution, both companies are ready to tackle emerging challenges in digital transformation.

Dr. Mazlan Abbas expressed his excitement about this partnership:
“We are on the brink of a new era in IoT and AI, and this collaboration marks a crucial step in bringing secure, intelligent solutions to businesses across industries. Aswant Solutions’ strong foothold in IT security and distribution perfectly complements our IoT capabilities, making this a powerful synergy for the future.”

Nor Asrul Mohd Noor echoed the enthusiasm, stating:
“At Aswant Solutions, we believe in innovation with security at its core. Partnering with Favoriot allows us to enhance the reach of AI-powered automation and IoT efficiency while ensuring robust protection for businesses. Together, we are driving the next wave of digital transformation.”

Unlocking New Possibilities in Smart and Secure Technologies

Favoriot is widely recognized for its scalable IoT platform, which enables businesses to integrate real-time data analytics and smart applications seamlessly. As a Kaspersky Platinum Partner in Malaysia, Aswant Solutions is a leader in cybersecurity, system integration, and IT distribution, providing businesses with the tools to navigate the evolving digital landscape securely.

This partnership is a leap forward in fostering technological advancements, allowing businesses in Malaysia and beyond to embrace AI, IoT, and cybersecurity with confidence.

About Favoriot

Favoriot is a premier IoT platform provider that simplifies the deployment of IoT applications, offering real-time data insights, cloud-based solutions, and seamless device integration.

About Aswant Solutions

Aswant Solutions is a leading IT security and distribution firm in Malaysia, specializing in advanced cybersecurity, networking, and system integration to help businesses secure their digital transformation journey.

For further details, visit:
🔗 Favoriot: www.favoriot.com
🔗 Aswant Solutions: www.aswant-solution.com