AI-Powered Predictive Intelligence for IoT Devices

Our embedded AI models predict function call success rates in real-time, enhancing reliability and security for all IoT applications.

Now Available for ESP32 Devices!

Our first implementation is ready for deployment on ESP32 microcontrollers.

Embedded AI Intelligence

Our AI models are integrated directly into IoT device firmware, providing real-time predictive analytics without cloud dependency. First available for ESP32, with more platforms coming soon.

Network Communication Success Prediction

Our AI model analyzes network conditions in real-time to predict the success probability of upcoming communication attempts, allowing your application to make intelligent decisions before initiating critical data transfers.

Predicts connection stability before data transmission
Reduces failed communication attempts by up to 78%
Optimizes retry strategies based on historical patterns
Adapts to changing network environments automatically

How Our IoT AI Technology Works

Lightweight machine learning models embedded directly in firmware, optimized for resource-constrained devices

Embedded in Firmware

Our AI models are integrated directly into the ESP32's firmware, requiring minimal resources while providing maximum benefits.

Real-time Predictions

Continuous analysis of system state provides instant predictions about function call success probabilities.

Adaptive Learning

Models improve over time by learning from actual device usage patterns, becoming more accurate with each operation.

Easy Integration with Your IoT Projects

Simple API calls to access powerful predictive capabilities, now available for ESP32

example.cpp

#include <freertos/FreeRTOS.h>
#include <freertos/task.h>
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "otainfo_predictor.h"

predictor_reply pr;
extern "C" void app_main(void) {
  model_setup();
  while (true) {
    predict(&pr);
    // trigger one inference every 500ms
    MicroPrintf("Score: %d", pr.score);
    if (pr.inadequate_on_cputemperature){
        MicroPrintf("Check CPU temperature!!");
    }
    if(pr.insufficient_freeheap) {
        MicroPrintf("Check memory usage");
    }
    if(pr.insufficient_on_lbs){
        MicroPrintf("1.2 MB OTA download needs atleast 32KB of single heap block. For smaller downlaod it may work");
    }
    if(pr.insufficient_freeheap){
        MicroPrintf("1.2MB download over https requires 80-100 kb of heap size");
    }
    vTaskDelay(pdMS_TO_TICKS(500));
  }
}

JUST LAUNCHED

ESP32 Implementation Now Available

Our first IoT AI prediction engine is ready for deployment on ESP32 microcontrollers. Get started today with our comprehensive SDK and documentation.

Complete SDK for ESP32 integration
Pre-trained models optimized for ESP32 architecture
Minimal memory footprint (only 32KB)
Compatible with Arduino and ESP-IDF frameworks

Memory Monitoring

Heap Stats

Network Monitoring

WiFi RSSI

Prediction Time

5msAverage

Ready to enhance your IoT devices with AI?

Get started with our ESP32 implementation today, and stay tuned for upcoming support for additional IoT platforms.