Ready-to-Use IoT Cloud & Applications
Low-Code & No-Code Firmware and Mobile Applications
Designed For Massive Scale & Advanced Algorithms
Advantages
Akhila Labs supports a wide spectrum of healthcare and wellness applications:
PLATFORM SUPPORT
AkhilaFlex provides ready-to-use framework components to achieve extensive connectivity and implementation efficiency.
STABILITY
Take advantage of proven framework components deployed in the field for more than 100000 devices with parallel users using it.
LOW-CODE
Application components and unified distributed architecture lets you focus on application development with API calls caring about the underlying architecture.
READY TO LAUNCH APPS
Your device is ready for sale in three simple steps.
1. Integrate device to Cloud.
2. White label your logo and Device logo to App.
3. Deploy on App Stores.
RANGE OF COMPONENTS
A wide range of components covers different IoT layers from the network, middleware, to applications.
COST EFFECTIVENESS
Save on hundreds of dollars by adapting ready to use IoT frameworks.
Architecture
Multi-layered AkhilaFlex framework is designed based on the concept of “No-Code Low-Code” needs of the distributed system components and deployments for IoT. It enables reliable adaptive interconnection between end-devices and the cloud as well as seamless integration with third-party ecosystems.
Apps Across Every Platform
Secure, scalable applications built across mobile, web, IoT management, analytics, and voice platforms to support connected products end to end.
- 5G Gateways
- Robotics
- Sustainable Products
- Battery Management
- 5G Gateways
- 5G Gateways
- 5G Gateways
- 5G Gateways
- 5G Gateways
Product Capabilities
AkhilaFlex 2.0 is engineered to power intelligent, connected products
across edge, cloud, and autonomous systems at scale.
Platform Modules
A flexible set of modular capabilities for managing devices, connecting hardware platforms, and enabling intelligent, rule-driven actions.
Platform Management &
Core Modules
A comprehensive set of platform capabilities for managing users, devices, data, integrations, and enterprise workflows at scale.
ScalabilityScalabilityScalabilityScalabilityScalabilityScalabilityIoT Cloud Applications
Visual-Inertial Odometry (VIO):
VINS-Fusion or OpenVINS tightly couple camera tracking with high-rate IMU data
to deliver centimeter-level positioning without GPS.
LiDAR Mapping:
LIO-SAM or Fast-LIO2 with 3D LiDARs (Velodyne, Ouster, Livox) generate real-time
3D occupancy maps and loop closures, even in dark or featureless environments.
Loop Closure & Relocalization:
Visual loop detection and global relocalization enable drift recovery and
reliable operation in large, complex spaces.
Device Analytics
Local Planning (ROS 2 Nav2):
PX4-Avoidance and the ROS 2 Navigation Stack use depth cameras
(Intel RealSense D435i, Stereolabs ZED) to build a local 3D voxel grid
and dynamically avoid obstacles without pre-mapped environments.
Global & Real-time Planning:
A* and RRT* algorithms generate collision-free paths while real-time
replanning at 10–20 Hz enables instant adaptation to new obstacles
and mission changes.
Ready to Use Mobile Apps
Short-range Telemetry:
900 MHz / 2.4 GHz SiK radios provide reliable command, control,
and low-latency feedback.
BVLOS Backhaul:
LTE/5G with VPN or ZeroTier enables secure long-distance operations.
Edge–Cloud Split:
Local autonomy is combined with cloud-based route planning,
geofencing, fleet analytics, and anomaly detection.
No-code Low-code IDE & Hardware
FPGA Integration:
Using Xilinx Kria or Zynq platforms, we implement hardware-accelerated image
processing pipelines to offload compute-intensive tasks from the CPU.
NPU Optimization:
Neural networks are optimized with TensorRT on NVIDIA Jetson for high
frame-rate inference with low power consumption.
Multi-core Scheduling:
Workloads are distributed across the flight controller, companion computer,
and accelerators using ROS 2 nodelets and custom executors to minimize
latency and avoid overload.
Planet Scale Infrastructure
FPGA Integration:
Using Xilinx Kria or Zynq platforms, we implement hardware-accelerated image
processing pipelines to offload compute-intensive tasks from the CPU.
NPU Optimization:
Neural networks are optimized with TensorRT on NVIDIA Jetson for high
frame-rate inference with low power consumption.
Multi-core Scheduling:
Workloads are distributed across the flight controller, companion computer,
and accelerators using ROS 2 nodelets and custom executors to minimize
latency and avoid overload.
What Drives Us to Build Better, Faster, and Smarter
Step 01
Connect your Hardware with AkhilaFlex Cloud
Step 02
Customise AkhilaFlex App UI for Product
Step 03
Deploy the App for your Customers
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