DATOMS has raised ₹25 crore (approximately $2.76 million) in a Series A funding round led by Vietnam-based investment firm Big Capital JSC. The round also saw participation from IvyCap Ventures and a follow-on investment from YourNest Venture Capital.
Previously, the company had secured ₹3.5 crore in a pre-Series A round with participation from Operators Studio and APT Research. Existing backers include Ridik Capital, Warmup Ventures, and BeyondSeed.
Founded in 2021 by NIT Rourkela alumni Amiya Samantaray, Asish Sahoo, Nataraj Sahoo, and Amrit Biswal, DATOMS provides a unified intelligence layer that converts physical machines into connected, trackable assets throughout their lifecycle. The platform enables enterprises and OEMs to connect machines, monitor real-time performance, predict equipment failures, optimise energy consumption, and streamline service operations across multiple stakeholders.
The startup addresses a persistent challenge in industrial ecosystems where machine data is fragmented across OEMs, enterprises, service partners, dealers, and financiers. Such data silos often force organisations into reactive decision-making and limit visibility into asset performance and lifecycle efficiency.
With the fresh capital, DATOMS plans to strengthen its product and core technology stack, enter new markets, and expand teams across engineering, data science, operations, and enterprise sales. The company will also advance its AI and analytics capabilities, enhance automation features, and accelerate the development of predictive maintenance, performance optimisation, and energy management solutions.
“We believe the next decade belongs to intelligent machines. This funding accelerates our mission to transform industrial assets into connected, self-aware systems that deliver measurable business outcomes. This investment validates our vision of turning connected machines into intelligent economic participants,” said Amiya Samantaray, Founder and CEO.
Vikram Gupta, Founder & Managing Partner, IvyCap Ventures, said, “We are pleased to support DATOMS as it scales its Industrial IoT and AI-driven platform for asset-intensive industries. The team has shown strong execution in building solutions that enhance efficiency, optimize performance, and unlock measurable value. We look forward to partnering with them as they expand their capabilities and market presence.”
Girish Shivani, Executive Director and Fund Manager at YourNest Venture Capital, added, “We believe the next decade will be defined by how effectively organizations harness their data. DATOMS is building critical infrastructure at the intersection of data and AI, enabling enterprises to unlock measurable value from complex data ecosystems. The market tailwinds are strong, and DATOMS has demonstrated both product-market fit and execution capability. We look forward to supporting their ambitious roadmap.”
Operating at the intersection of Industrial IoT, energy technology, and enterprise SaaS, DATOMS serves asset-intensive industries including quick commerce dark stores, logistics warehouses, healthcare facilities such as MRI and CT scan rooms, as well as industrial plants, commercial buildings, and infrastructure operators across cement, steel, mining, and manufacturing sectors. Its platform helps reduce downtime, lower energy usage, and improve operational efficiency by integrating directly with OEM machines for lifecycle monitoring.
Over the past year, the company has more than doubled its revenue as enterprise customers expanded usage. DATOMS currently monitors over 25,000 machines and aims to scale that number to one lakh within a year, signalling a shift toward repeatable enterprise-wide deployments. In the next three years, the startup plans to deepen engagement with existing clients, enter new industries, enhance AI-driven prediction models, expand across India, and scale internationally. With offices in Bhubaneswar and Bengaluru, DATOMS serves more than 100 customers globally and is positioning itself as a leading operational intelligence platform for connected machines.
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