The MSME sector is often considered the backbone of Indian economic growth: powering manufacturing output, generating employment, and anchoring supply chains across sectors. MSMEs employ approximately 11 crore people, making it the second-largest employer in the nation after agriculture, and contributes to roughly a third (30%) of the country’s GDP. They are an integral partner to labour-intensive businesses like textiles, garments, food processing, specialty chemicals, engineering parts, etc, contributing significantly to national output and exports.
But as technology and manufacturing become more complex and innovation becomes key, how do MSMEs operate more efficiently, scale sustainably, and survive in a highly competitive market? Microverse Automation Private Limited addresses this gap by delivering practical, cost-effective automation and digital control solutions tailored for Indian MSMEs.
Microverse provides advanced automation systems for India’s process industries, from Distributed Control Systems (DCS) and PLCs to IIoT, predictive analytics and cloud integration.
Their products and services help MSMEs and other companies improve productivity, reduce downtime, and build resilient operations. What sets Microverse apart are their tailored solutions, designed and manufactured in India, for Indian businesses. (Their client roster includes bluechips and listed companies like Grasim, Vedanta, Hindalco and Marico to small caps like Deepak Nitrite, Sarda Energy & Minerals, Dhampur Bio Organics, Maithan Alloys and others.)
We spoke to Siddharth Mehendale (Director, Technology and Projects) at Microverse about the role they play in strengthening the performance of India’s MSME manufacturing base.
Smallcap Spotlight: Microverse was founded 1989 to build indigenous automation systems at a time when India’s process industries relied heavily on foreign suppliers. What gaps did you see back then, and how did you manage to get Indian firms to switch to a homegrown process automation company?
Siddharth Mehendale: Back then, the gap wasn’t just “imported vs Indian.” It was fit-for-purpose vs force-fit.
India’s process industries – especially MSMEs – operate under a very different set of constraints: mixed-age equipment, wide variations in operator skill, inconsistent utilities, tough ambient conditions, aggressive cost sensitivity, and plants that must continue to run even when spares and service are difficult to access quickly. Many global systems are technically strong, but they’re typically designed around more standardised plants, predictable infrastructure, and highly structured maintenance ecosystems. When you transplant those assumptions into India, you often end up with either over-engineering that is expensive, or under-adoption because it doesn’t match how the plant actually runs.
Microverse created a niche because our solutions were born in Indian plants, not adapted in a boardroom. We built a robust core automation platform, and then layered practical customisation that Indian users genuinely needed – without compromising on safety, reliability, or engineering discipline. That approach built trust: customers saw that we understood their reality, and that the system would work on day one and stay serviceable for years.
A good example is how automation has to coexist with the operations culture. In many Indian MSMEs, manual labour is available, and certain workflows are traditionally handled manually. If you try to “replace people” overnight, you get resistance. Hence, our approach was staged: keep operators in the loop, digitise visibility first, and then automate the critical and safety-relevant loops to global standards. Customers could see immediate value – better uptime, fewer quality deviations, safer operations – without a disruptive change management shock.
Finally, switching happens when risk goes down. We reduced risk through local support, faster turnaround, availability of spares, pragmatic training, and ownership-level accountability. Over time, customers realised they weren’t just buying a product – they were gaining a partner that could respond quickly, iterate with them, and deliver the same outcomes they expected from global suppliers, but with far better alignment to Indian operating conditions and economics.
SCS: How has Microverse engineered its solutions to cater to Indian MSMEs? What role does a company like Microverse play in making advanced automation more accessible to smallcaps and MSMEs?
SM: Microverse has engineered its solutions for Indian MSMEs with a simple principle: make automation adoptable in phases, not as a one-time “big bang” project.
First, our platforms are intentionally designed to be modular and scalable. MSMEs don’t need, or can’t justify, implementing the full enterprise automation stack on day one. We design the system so a customer can start with exactly what they need today and expand later, without ripping out what was installed earlier. That modularity applies across hardware, control, historian, alarms, dashboards, and higher-layer analytics.
Second, we align the deployment to the customer’s real objective, not a generic automation checklist. Different MSMEs have different starting points:
- Some want end-to-end automation for safety, quality, and throughput.
- Some want visibility and traceability first – instrumentation, basic data acquisition, dashboards, and audit trails – because transparency itself drives better decisions and faster troubleshooting.
- In batch industries, many start by stabilising the batch sequence and reporting, and then move toward batch optimisation – for example, identifying a “golden batch” and reducing variance.
- More advanced customers may leapfrog straight into analytics and AI, using data to improve yield, energy efficiency, and predict deviations, without immediately changing core control philosophy.
Because our stack is modular, we can offer that “pick-and-choose” pathway in a structured way: lean, cost-effective, and engineered for expansion. In other words, we offer tailor-made flexibility to pick and choose only the necessary modules, enabling us to provide lean, modular, and cost-effective solutions.
The broader role a company like Microverse plays is to reduce the barriers that typically keep MSMEs out of advanced automation. For instance, with us, they have lower adoption risk (phased implementation means lower capex and faster ROI), lower integration friction (we make it work with existing legacy machines and mixed-vendor setups, which is the reality for MSMEs), lower operational complexity (operator-friendly design, practical training, and support that’s accessible), and higher reliability per rupee (rugged design and serviceability suited to Indian conditions).
In short, we help MSMEs move from manual or partially automated operations to modern automation in a way that matches their economics and plant reality: starting small, proving value quickly, and scaling confidently.
SCS: Many smallcap industrial companies operate in niche fields like specialty chemicals, ethanol, metallurgy etc. How does process automation become a competitive advantage for these firms, and how does Microverse deliver an impact (cost savings, overall efficiency etc).
SM: For niche smallcap industrial firms, process automation isn’t a “nice-to-have”; it can directly improve margin, consistency, compliance, and scalability. In speciality chemicals, ethanol, metallurgy, and similar sectors, a small improvement in yield, energy use, batch-to-batch repeatability, or downtime can move the needle meaningfully because these businesses often operate on tight windows: raw material variability, stringent quality specs, safety requirements, and high cost of unplanned shutdowns.
The competitive advantage typically builds in layers.
Step one is visibility and transparency. Before you can reduce cost or improve efficiency, you need reliable, time-stamped process data – what actually happened, when it happened, and under what conditions. Once that foundation is in place, the plant stops running on anecdotes and starts running on evidence. This alone reduces losses: faster troubleshooting, fewer repeat mistakes, and better accountability across shifts.
Step two is targeted, high-impact automation. Instead of revamping everything, we focus on the 20% of loops and workflows that drive 80% of outcomes – safety-critical interlocks, bottleneck areas, energy-heavy sections, and quality-sensitive stages. When done with a cost–benefit lens, this can be extremely cost-effective. In many MSME deployments, we’ve seen payback timelines in the 9–12 month range because the gains show up quickly as reduced downtime, fewer off-spec batches, improved throughput, and lower energy consumption.
A common misconception is that automation must be financially heavy and operationally disruptive. In reality, when approached correctly, it’s a phased journey: start with measurement and visibility, then automate the critical points, then standardise recipes or operating windows, and finally move into optimisation and advanced analytics. This minimises disruption while continuously compounding returns.
That’s where Microverse delivers impact. We help customers design and execute a 5-year roadmap starting with quick wins and building toward full automation and optimisation. We stay involved beyond installation: performance tuning, operator enablement, reliability improvements, and incremental upgrades as the plant evolves. Many of our customers have stayed with us for 15–20 years because automation isn’t a one-time purchase; it’s a lifecycle partnership. The best part is that the savings generated in year one and year two often fund the next phase, creating a self-reinforcing path toward world-class operations without overburdening the business upfront.
SCS: How do you see AI/ML-driven predictive models and intelligent automation shaping the next phase of India’s MSME and smallcap manufacturing growth?
SM: AI/ML-driven predictive models will be a step-change for Indian MSMEs because they turn manufacturing from experience-led heuristics into data-driven, closed-loop optimisation without requiring MSMEs to become “AI companies.”
For decades, plant performance in MSMEs has depended heavily on operator intuition and tribal knowledge. That intuition is valuable, but it’s also non-scalable, shift-dependent, and hard to codify. The last few years have changed the economics completely: computing is cheaper, open-source model ecosystems are mature, and industrial data pipelines are easier to deploy. As a result, MSMEs can now justify building models that previously only large enterprises could afford.
Where it becomes transformative is in the type of modelling now possible. Modern industrial AI is not just basic trend prediction – it’s multivariate, context-aware inference across the entire plant stack. In practice, we’re moving toward three things:
- Hybrid models that blend first-principles constraints with ML (physics-informed and constraint-aware learning), so outputs remain physically plausible and actionable.
- Representation learning across heterogeneous signals (process, electrical, instrumentation, quality, maintenance), enabling models to capture latent plant states instead of relying only on explicit tags.
- Time-series architectures that learn lagged causality and temporal dependencies, moving beyond correlation to operationally meaningful leading indicators.
This matters because MSME knowledge is often siloed. Process experts know the chemistry, automation teams know the control loops, electrical teams understand power quality issues, maintenance teams know failure patterns. A well-designed model becomes a cross-domain knowledge engine – it fuses signals, discovers non-obvious dependencies, and outputs a shared “single truth” about what drives yield, energy intensity, throughput, and risk. Once those relationships are learned, the plant gains something game-changing: early-warning signals, root-cause probabilities, and operating-window recommendations that are consistent across shifts and sites.
The critical nuance is adoption. Industrial AI fails when it’s treated as a black box. The “last mile” is socio-technical: you have to operationalise models with human-in-the-loop workflows, where operator experience is used to label regimes, validate anomalies, and define safe action boundaries. In other words, the goal isn’t to replace operators; it’s to augment decision-making with explainable insights, confidence intervals, and guardrails that respect real-world constraints.
This is exactly where Microverse and our AI/ML platform fit in. Our platform is built to sit on top of SCADA (Supervisory Control And Data Acquisition) and historian data to deliver:
- Predictive analytics (forecasting deviations, predicting quality drift, detecting precursor patterns to failures),
- Prescriptive analytics (recommended set points and operating windows under constraints),
- Real-time operator guidance (safety envelopes, adherence scoring to a “golden batch,” and contextual alerts rather than alarm floods).
In the next phase, the winners among MSMEs and smallcaps will be the ones that move from “monitor and react” to predict, prescribe, and continuously optimise – using AI not as a buzzword, but as an operational layer that improves OEE (Overall Equipment Effectiveness), reduces energy per ton, stabilises quality, and institutionalises expertise into a scalable system.
Learn more about Microverse and their solutions here.