From rice mills to farms, IoT devices from a Karnataka startup are transforming data into smarter decisions through AI, cloud dashboards, and voice guidance. How is this possible? Ananth S. Kulkarni of Hexitronics tells their story of empowering small businesses through these devices to EFY’s Nidhi Agarwal.
Q. What does your company do?
A. We design and manufacture IoT (Internet of Things) devices, which now also serve as data sources for AI systems. Our devices are tailored to different use cases, delivering solutions for our customers. Currently, our clients are primarily small and medium-sized enterprises, such as rice mills and stone-crushing units, that require remote monitoring for their operations.
Q. Do you make your own products or only make them for clients as needed?
A. Earlier, we worked with clients on specific use cases because our products could not be produced in advance. But with our own architecture, we can now create them before a client even approaches us. The products are now universally configurable and usable across different use cases. We are also looking for distributors across India.
Q. Can you explain your architecture and the products you offer?
A. Our architecture consists of two parts. One is an IoT gateway device called NodeX, and the other is AutoStack, our own cloud platform. The gateway can connect to various factory sensors, such as moisture sensors, conveyor motor temperature sensors, energy meters, programmable logic controllers (PLCs), or variable frequency drives (VFDs), and collect data at set intervals. The data is then sent to our cloud platform, AutoStack, which automatically generates dashboards when the devices are switched on for the first time.
Q. What do you mean by AI devices?
A. In large factories, like cement plants, production spans multiple departments managed by supervisors who coordinate through meetings or urgent calls. The process relies heavily on human intervention to meet targets. With AI, sensors collect real-time data such as motor speed, conveyor speed, and temperatures, and ERP feed it to a backend. AI analyses these parameters, identifies trends, and recommends precise actions, such as adjusting conveyor speed to boost output. Such optimisation is complex to achieve manually and can now be paired with voice assistants for easier interaction. A practical example is agriculture, where farmers often struggle with seed and pesticide choices. Voice-assisted AI guides them on natural pest control, seed selection, and timely actions based on crop and weather conditions. It can also connect to soil-testing sensors, discuss soil health, and suggest measures to improve it. Similar AI assistants can support students, teachers, journalists, and lawyers, enabling faster decisions, better guidance, and higher productivity.
Q. When you say you are integrating AI into an IoT device, where exactly are the AI models running?
A. The AI models run on the cloud. The sensors send data to the gateway, which forwards it to the cloud, and the dashboard shows the output. Different AI architectures exist depending on the business goal. If the aim is revenue, the approach can be similar to services such as Perplexity. If the aim is solving real-world problems, an agent-style AI is used. In this setup, the edge device such as a phone, laptop, or Raspberry Pi calls an API from a server hosting an LLM such as GPT, Claude, Gemini, or DeepSeek. The response from the model is then converted into voice on the local device, letting it act like any persona the user chooses.
Q. How do you differentiate yourself from other companies building IoT and AI-enabled devices?
A. Hexitronics stands out by bringing all essential skills together. We carry hardware design, manufacturing, cloud services, data hosting, analytics, and AI integration, all under one roof. Most firms offer only one or two of these pieces, but we provide the entire stack. Also, we offer our IoT cloud services free of cost, with no subscription fees for customers.
Q. Who are your target customers?
A. The target customers are MSMEs such as rice mill owners, crusher plants, food processing units and small manufacturing units. This includes people who run a few CNC machines and take up machining jobs as vendors for larger companies. They want to track efficiency, find weaknesses, and improve operations. IoT devices help them get these insights end-to-end.
Q. What is your approach to prototyping a new industrial IoT solution?
A. Prototyping an industrial IoT solution requires patience and close collaboration with the client, especially during proof of concept stages. Field conditions often introduce challenges that are not evident in lab testing. For example, in a project with a gas outlet client that provides SaaS for LPG auto-rickshaws, we deployed 400 devices over a three-month POC. Issues included field noise, limited network availability, and hardware interactions, such as gas dispensers’ motherboards sharing data with our IoT device, which then feeds the client dashboard via MQTT. Environmental factors, such as power line noise from diesel generators affecting communication lines, caused data errors. These misinterpreted data points must be filtered or the hardware redesigned because accurate data is critical for AI-driven decisions. While simulations and lab tests often work perfectly, the real challenge arises in dynamic field conditions.
Q. How are electronics involved in your product, and what microcontrollers or hardware do you use?
A. The choice of hardware depends on the client’s requirements, budget, and the criticality of the data being handled. We evaluate data sensitivity for decision-making and safety, then select the microcontroller accordingly. In noisy environments, we design circuits with EMC and EMI compliance to reduce interference. Data is also validated at the gateway, which filters noise by comparing incoming data against average values to ensure accuracy before forwarding it. For most applications, we use ESP32-series microcontrollers, including ESP32 cameras, for tasks such as monitoring and surveillance. ESP32 supports various modules, such as Quectel and Qualcomm and is easy to code. For more computationally intensive tasks, we use Raspberry Pi models such as the Raspberry Pi Zero, Zero W, or Pi 4, depending on processing and RAM requirements. The hardware choice is always tailored to the use case.
Q. How do you decide whether to use an off-the-shelf module or build a custom solution for a hardware design?
A. The choice depends on the sensor or electronic component being integrated, such as PLCs, VFDs, energy meters, HMIs, or displays. Each device has specific output ports such as RS-485, RS-232, Ethernet, CAN, or Profinet. Our boards already have predefined footprints for various modules. Based on the customer’s equipment and communication needs, we select the appropriate pre-manufactured module, called a ‘feather’, and assemble it on the board.
Q. Where and how do you manufacture your PCB and hardware, and how do you handle assembly and enclosures?
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