SwitchOn, a Bengaluru-based manufacturing tech startup, has raised $8 million in a Pre-Series B funding round led by IvyCap Ventures. SIG Tattva and Trifecta Capital also joined the round.

The company builds AI-based quality inspection systems for factories. In simple words, its technology helps manufacturers find product defects automatically, using cameras and computer vision instead of depending only on manual checking.

This is a serious problem in manufacturing. A small scratch, wrong label, crack, missing part or surface defect can lead to rejected products, recalls, customer complaints and wasted material. SwitchOn wants to help factories catch these problems faster and more accurately.

What SwitchOn does

SwitchOn was founded by Aniruddha Banerjee and Avra Banerjee. The startup works in the industrial AI space, especially quality inspection for manufacturing lines.

Its flagship platform, DeepInspect, uses computer vision to inspect products moving quickly on production lines. Computer vision means AI that can “see” and understand images from cameras.

The company says its system can inspect more than 1,200 products per minute, deliver over 99 percent detection accuracy and reduce quality-related costs by up to 50 percent.

That is useful for sectors where speed and accuracy both matter, such as consumer goods, automotive, electronics and pharmaceuticals.

Funding details

SwitchOn has raised $8 million in Pre-Series B funding. The round was led by IvyCap Ventures, with participation from SIG Tattva and Trifecta Capital.

The startup plans to use the money for international expansion, stronger research and development, and bigger sales and go-to-market teams.

It will also invest in next-generation physical AI. Physical AI refers to AI systems that work with real machines, cameras, sensors and factory equipment, not only software screens.

Startup aim and purpose

SwitchOn’s aim is to make quality inspection faster, smarter and less dependent on human fatigue.

Manual inspection has limits. A person checking products for hours can miss tiny defects, especially on fast-moving lines. Some defects are also too small or too repetitive for human eyes to catch consistently.

SwitchOn’s purpose is not to remove people from factories completely. The better way to see it is this – AI handles the repetitive inspection work, while humans focus on decisions, maintenance, process improvement and quality control.

For example, if a packaged food company needs to check whether labels are printed correctly on thousands of packets, AI can scan each item quickly. If a car parts maker wants to detect scratches or surface defects, the system can flag doubtful pieces before they move ahead.

Why manufacturers need this

Factories are under pressure to produce more, waste less and keep quality consistent. A single bad batch can hurt margins and brand trust.

AI inspection can help in three ways. It can catch defects early, reduce rejected goods and create data about where problems are happening.

That last part is important. If defects are happening again and again at one machine or one stage, the factory can fix the root cause instead of only removing bad products at the end.

This is where SwitchOn’s technology can become more valuable than a simple camera setup.

Current reach and customers

SwitchOn says it has deployed systems on more than 170 production lines across over 60 manufacturing facilities in four continents.

Its customer list includes names such as Unilever, Bosch, Maruti Suzuki and ALPA. That gives the startup credibility because large manufacturers usually test such systems carefully before using them in production.

The company’s first production deployments started in 2021, and it has since moved beyond India into global markets.

Competitors

SwitchOn competes in a growing industrial AI and machine vision market.

Globally, companies such as Cognex, Keyence, Landing AI, Instrumental and other machine vision firms operate in related areas. In India, manufacturing automation and robotics startups are also building solutions for inspection, factory intelligence and process automation.

SwitchOn’s edge will depend on how well its system works in real factory conditions. Factory floors are not clean demo rooms. Lighting changes, products move fast, dust appears, machines vibrate and defect types can vary. The best system is the one that keeps working accurately after installation.

Why this funding matters

The funding comes at a good time for India’s manufacturing push. Sectors like electronics, automobiles, semiconductors, consumer goods and pharmaceuticals are all trying to improve quality and scale.

Government programmes such as Make in India and PLI schemes have also pushed companies to build more locally. As manufacturing grows, quality systems must grow with it.

For SwitchOn, this is a chance to become an Indian industrial AI company with global relevance.

Challenges ahead

The opportunity is big, but the work is hard. Selling to factories takes time. Each production line may need custom setup, testing and integration.

The company will also need to prove that its AI can handle different materials, shapes, speeds and defect types. A system that works well in one industry may need changes for another.

Cost will matter too. Manufacturers will adopt AI inspection only if it clearly saves money, improves quality or reduces downtime.

Conclusion – Key takeaways

SwitchOn’s $8 million Pre-Series B funding gives it fresh capital to expand its AI quality inspection systems across global manufacturing markets.

Founded by Aniruddha Banerjee and Avra Banerjee, the Bengaluru startup is using computer vision and physical AI to help factories detect defects faster and reduce quality-related losses.

Its bigger purpose is clear – make manufacturing quality more reliable, data-driven and less dependent on slow manual checks. If SwitchOn can scale without losing accuracy, it can become an important player in industrial AI.

Facts Input- YS

Tata Consultancy Services has launched a new Industrial AI-focused lab in Bengaluru, powered by NVIDIA’s AI infrastructure. The facility, called the TCS Autonomous Engineering Lab Powered by NVIDIA, is located at TCS’ Global Axis campus.

The launch is aimed at one problem many companies are facing right now. They are interested in AI, they run pilots, they test ideas, but moving from a small experiment to a real factory, vehicle platform or shop-floor system is still difficult.

TCS wants this lab to become a practical bridge between AI demos and actual industrial use. The focus is mainly on mobility, manufacturing, autonomous systems, smart plants and engineering-heavy businesses.

What TCS has launched

The new Bengaluru lab will work as a physical hub for Industrial AI innovation. It will help companies design, test, simulate and validate AI-led solutions before using them in real operations.

That last part matters. In manufacturing or mobility, a bad rollout can be expensive. If an AI system fails inside a plant, production may stop. If an autonomous mobility feature is not tested properly, safety becomes a concern. This is why simulation and controlled testing are important.

The lab will use NVIDIA AI infrastructure and TCS’ own industrial engineering experience to help customers build solutions faster and with lower deployment risk.

Why Bengaluru was chosen

Bengaluru is a natural place for such a lab. The city has a deep technology ecosystem, engineering talent, automotive software teams, startups, global capability centres and manufacturing-tech expertise.

For TCS, Bengaluru also gives access to clients, partners and skilled teams that understand both software and industrial engineering. That mix is important because Industrial AI is not only about writing code. It needs knowledge of machines, sensors, vehicles, plants, safety and operations.

What Industrial AI means

Industrial AI simply means using artificial intelligence in physical industries. It is AI for factories, machines, vehicles, supply chains, energy systems and field operations.

A normal office AI tool may summarize emails or write reports. Industrial AI may predict when a machine will fail, inspect product defects through cameras, improve factory output, help vehicles understand roads or simulate how a product will behave before it is built.

This is harder than basic chatbot AI because the system has to work with real-world data, machines, sensors and safety rules.

Key solutions inside the lab

TCS has highlighted several solution areas for the lab.

One is TCS DriveSphere, an AI-led mobility platform for software-defined vehicles. It uses digital twins, real-time data, predictive analytics and over-the-air lifecycle management.

In simple words, it can help vehicle makers manage cars that behave more like connected software platforms. This is becoming important as modern vehicles get more sensors, software updates and driver-assistance features.

The lab will also support AI-led mobility and autonomous systems, including ADAS, autonomous driving, perception systems and intelligent decision-making.

For manufacturing, it will work on physical AI and smart factory use cases such as predictive maintenance, automated quality inspection and real-time process improvement.

The lab will also cover agentic AI and vision AI. Agentic AI can take multi-step actions toward a goal, while vision AI can read images or video from cameras. In a factory, that could mean spotting defects on a production line. In service operations, it could mean helping teams understand problems faster.

Why NVIDIA is important here

NVIDIA is not only a chip company now. Its AI hardware and software stack is used widely for training, simulation, robotics, autonomous driving and industrial AI workloads.

Industrial AI needs heavy computing power. Simulating a vehicle, running vision models or testing a factory process digitally can require strong GPUs and specialized AI tools.

By using NVIDIA’s full-stack AI platform, TCS can offer customers a more serious testing environment. This can help companies try difficult AI use cases before spending money on full deployment.

What companies may gain

For enterprises, the lab can reduce guesswork.

A carmaker can test ADAS-related software in simulation before moving closer to road testing. A manufacturer can test whether vision AI can catch defects on a production line. A plant operator can see if predictive maintenance can actually reduce downtime.

This is useful because many AI projects fail after the pilot stage. They look impressive in a presentation, but struggle when connected to real machines, messy data and business deadlines.

The TCS lab is meant to shorten that distance.

Why this move matters for TCS

TCS is India’s largest IT services company and was founded in 1968 as part of the Tata Group. The company has been trying to deepen its AI services business as clients shift budgets toward automation, cloud, data and AI.

This lab gives TCS a more visible industrial AI asset. Instead of only telling clients that AI can help them, it can bring them into a lab, test use cases and show working prototypes.

That is important because IT services companies are under pressure to prove that AI is not just a consulting story. Clients want results, not slides.

Competitors and market context

TCS will compete with companies such as Infosys, Wipro, HCLTech, Tech Mahindra, LTIMindtree, Accenture, Capgemini and IBM in the industrial AI space.

Several of these firms are also building AI labs, industry clouds, automation platforms and partnerships with chip or cloud companies. The difference will come from execution – who can take AI from experiment to measurable business impact.

For manufacturing and mobility clients, the winners will be partners who understand both software and industrial reality. A factory floor is not a clean test environment. Neither is a road.

Challenges ahead

The biggest challenge is making AI reliable in real operations.

Industrial data is often incomplete, old or scattered across systems. Machines may be from different vendors. Plants may have legacy equipment. Safety checks can slow deployment. Workers also need training to trust and use AI tools.

Another challenge is cost. AI infrastructure is expensive, and companies will want proof that the investment saves money, improves quality or increases output.

TCS will need to show real examples where its lab helps clients move faster and avoid costly mistakes.

Conclusion – Key takeaways

TCS’ NVIDIA-powered Industrial AI lab in Bengaluru is a serious move toward practical AI adoption in mobility and manufacturing.

The lab will help companies prototype, simulate and validate AI solutions before full deployment. Its focus areas include software-defined vehicles, ADAS, autonomous systems, smart manufacturing, predictive maintenance, vision AI and digital twins.

For TCS, the lab strengthens its AI services story. For enterprises, it offers a safer place to test industrial AI before taking it to the real world.

Facts Input- TCS PressRelease