In India, IoT, telematics, and artificial intelligence are reshaping sustainability from a reporting requirement into a core business driver. Mr. Alok Kumar, Director and Founder of Saarthi Greentech, shared that by making operational inefficiencies visible in real time, these technologies help organisations reduce fuel consumption, improve asset utilisation, and lower emissions—directly linking sustainability to financial performance. In India’s cost-sensitive, asset-heavy environment, digital adoption succeeds only when it delivers efficiency, cost savings, and verifiable decarbonisation together. Saarthi Green’s India-ready technology combines rugged edge devices, engine-level sensors, and cloud analytics to deliver actionable insights. Sensor-driven MRV systems further strengthen ESG credibility by producing audit-ready data aligned with BRSR and global investor expectations. In an exclusive interview with Co-founder Editor, NewzOnClick, highlighted how technology-led decarbonisation is transforming India’s industrial and logistics sectors.
In the Indian context, how are IoT, telematics, and AI reshaping sustainability initiatives and operational efficiency across sectors such as logistics, manufacturing, and infrastructure?
In India, IoT, telematics, and AI are doing two fundamental things: they are making invisible inefficiencies visible, and they are turning sustainability from a CSR conversation into a P&L conversation. In logistics, connected engines and vehicles allow us to monitor fuel consumption, idling, driving behaviour, load patterns, and route efficiency in real time. That directly translates into fewer empty runs, reduced pilferage, lower diesel use, and therefore lower CO₂ and particulate emissions.
In manufacturing and infrastructure, IoT and AI are helping operators move from reactive maintenance to predictive and condition-based maintenance. When you continuously monitor vibration, temperature, load, run hours, and fuel efficiency of engines, DG sets, and other critical assets, you can optimise when and how they are used. The same data that improves uptime and reliability becomes the basis for credible Scope 1 emission baselines and reduction plans.
The Indian context is unique because our markets are cost-sensitive and asset-heavy. So digitalisation succeeds when it can simultaneously deliver operational efficiency, lower fuel and maintenance costs, and verifiable decarbonisation. That “triple benefit” is what we focus on at Saarthi Green.
Can you briefly explain Saarthi Green’s real-time fuel and emissions monitoring architecture and how it scales across India’s diverse operating conditions?
Our architecture is built ground-up for Indian conditions – noisy, dusty, hot, with patchy connectivity and very diverse engine types.
At the edge layer, we integrate with the engine through a combination of sensors (fuel usage, hydrogen flow, temperature, RPM, load, pressure) and native data (CAN bus, GPS, run hours). This is connected to a rugged automotive-grade IoT device installed on trucks, DG sets, or marine engines. The device collects high-frequency telemetry, runs basic rules locally, and uses 4G to send compressed data packets to the cloud whenever connectivity is available, with store-and-forward when it is not.
At the cloud layer, data is ingested into a scalable data pipeline where it is cleaned, normalised, and enriched with engine-specific factors and emission coefficients. On top of this, we run analytics and AI models that estimate fuel efficiency trends, detect anomalies, and calculate CO₂ and particulate reduction attributable to our hydrogen retrofit systems.
At the application layer, we provide dashboards for fleet owners, plant heads, sustainability teams, and management. They can see per-asset and per-site performance, compare pre- and post-retrofit data, and download reports that feed directly into ESG and internal reporting.
Because the system is modular and hardware-agnostic, we can deploy the same architecture across trucks, buses, DG sets, and marine engines, and scale it across India’s varied terrains and operating profiles.
Given India’s evolving ESG regulations, how can sensor-based, verifiable digital data replace manual reporting and improve credibility in sustainability disclosures?
One of the biggest pain points in ESG today is that much of the reporting is still based on estimates, spreadsheets, and manual consolidation. This creates three issues: lack of timeliness, lack of granularity, and questions about credibility.
Sensor-based digital data changes that. When fuel consumption, engine run hours, load, and operating profiles are captured automatically at source, you are no longer dependent on assumptions and monthly summaries. Instead, you have a continuous digital trail that links every litre of fuel used to a specific asset, location, and time stamp.
For Indian companies responding to frameworks like BRSR and global investors asking for audit-ready ESG disclosures, this matters a lot. A digital, sensor-driven MRV (Measurement, Reporting and Verification) layer can:
- Provide tamper-evident records of emissions and savings,
- Enable independent audits based on raw data rather than static PDFs, and
- Allow scenario analysis – for example, how fuel and emissions change with route, shift, or asset type.
Ultimately, this moves sustainability reporting from “claimed numbers” to “computed numbers”. That shift in credibility is going to be very important as regulations tighten and capital gets linked to ESG performance.
How are digital twins and predictive maintenance being adopted in Indian industries to reduce downtime, cut fuel consumption, and improve overall asset performance?
We are at an interesting inflection point. Large, sophisticated players in sectors like power, oil & gas, metro rail, ports, and process industries are already experimenting with digital twins for critical assets. At the same time, many mid-sized enterprises are just starting with basic IoT and condition monitoring.
A digital twin of an engine, DG set, or fleet asset essentially combines its design parameters with real operating data. When you stream telemetry into that model, you can identify when the asset is drifting away from optimal efficiency – for example, higher fuel use at the same load, abnormal temperatures, or irregular vibration patterns. Predictive maintenance models then trigger alerts before a failure happens.
For operators, this means fewer breakdowns, better planning of service schedules, and more stable fuel efficiency over the life of the asset. When you overlay a hydrogen retrofit like Saarthi’s on top of this digital twin, you can actually simulate and then measure how the asset behaves before and after retrofit, both in terms of performance and emissions.
In the Indian context, adoption will accelerate as these tools are packaged in simpler, outcome-focused offerings – not just big digital projects, but “payback-linked” solutions that combine uptime, fuel savings, and emission reduction.
What are the key challenges in scaling IoT-driven decarbonisation across India’s logistics networks, heavy industries, and public infrastructure, especially in cost-sensitive markets?
There are several layers of challenge.
First is economic perception. Many operators still see IoT and decarbonisation as cost centres rather than ROI-positive investments. If you do not clearly translate data and emissions into fuel savings, maintenance reduction, or risk mitigation, adoption stalls.
Second is fragmentation. India’s logistics ecosystem is dominated by small fleet owners and subcontractors. Public infrastructure assets are often managed by multiple stakeholders. Deploying a unified digital and decarbonisation layer on such fragmented ownership structures needs very thoughtful commercial models.
Third is operational capability. Rolling out IoT devices, sensors, and hydrogen retrofits at scale requires a trained network for installation, calibration, and after-sales support. Without this, the technology either underperforms or is abandoned.
Fourth is trust and data literacy. Many users have had poor experiences with low-quality gadgets or unscientific “fuel-saving devices”. We must rebuild trust by using certified hardware, transparent data, and third-party validation.
Our response at Saarthi has been to design simple, rugged solutions, back them with strong field support, and offer outcome-linked models like Decarbonisation-as-a-Service, where we take on part of the performance risk and align our incentives with the customer’s savings and ESG outcomes.
Looking ahead, what role do you see Saarthi Green playing in helping Indian enterprises achieve measurable, technology-driven sustainability outcomes at scale?
I see Saarthi Green as a bridge player in India’s energy transition – sitting between today’s diesel-heavy reality and tomorrow’s low-carbon ecosystem. The reality is that diesel engines, gensets, and marine assets will not disappear overnight. Our role is to make every one of those engines as clean, efficient, and data-visible as possible, starting now.
We do this through three pillars:
- Hydrogen-enabled retrofits that directly reduce fuel consumption and tailpipe emissions on existing assets.
- IoT and carbon intelligence that turn each engine into a measurable node in the ESG and operational performance story.
- Outcome-linked business models like DaaS, which let enterprises adopt advanced decarbonisation without heavy upfront capex.
As we scale across logistics, manufacturing, ports, construction, and critical infrastructure, our aim is to be the partner of choice for enterprises that want proof, not promises – hard numbers on fuel saved, tonnes of CO₂ avoided, and particulate matter reduced.
In the longer term, we see Saarthi GreenTech contributing to standards, best practices, and digital MRV frameworks that can be used not just in India but in other emerging markets facing similar challenges. If we can prove that a home-grown, hydrogen-and-IoT-powered approach can deliver measurable climate impact at scale in India, it will be a powerful blueprint for the global South.
