Aniruddha Banerjee, co-founder and CEO of SwitchOn, sits down with Rohit Yadav to explain why factory floors got automated decades ago while quality inspection stayed manual, and what finally changed. He left Nvidia in San Francisco. His co-founder, who is also his first cousin, left a team building India’s moon rover. They started in a Bengaluru apartment, working on a problem their own family’s manufacturing business knew well.

SwitchOn builds AI for quality inspection, aiming at zero defect and zero customer complaint. The core product, Deep Inspect, combines cameras, lighting and compute at the line. Since commercialising in 2021 the company has grown roughly 3x year over year, and now works with 37 clients across about 80 sites and close to 300 deployments, with a team near 100 and around 40 percent of revenue from Europe, Southeast Asia, and North and South America. The system detects defects as small as 150 microns, runs at up to 1,200 parts per minute, holds false positives under 0.5 percent, and lets customers train and deploy their own models in 45 minutes.

Whether you build hardware, invest in physical AI, or run operations in a plant, this one is about the unglamorous layer where most of the value actually sits.Chapters:

00:00 Two cousins, a family manufacturing business, and the quality gap

02:05 Why computer vision and inspection took so long to meet

04:01 What SwitchOn builds, and what came after Deep Inspect

05:27 The numbers: 37 clients, 80 sites, 300 deployments

08:25 Where deployments actually run into trouble inside a plant

11:58 Training on good product instead of defect data

15:23 Why inference runs at the line and not in the cloud

18:29 What stops a pilot from scaling inside a large manufacturer

22:07 Advice for founders: stakeholders, and willingness to pay

24:19 Hardware versus software in physical AI

27:38 The mid-market question, and why they have not gone there yet

31:13 Going global: product fit, referrals, and earning trust

34:50 The moat: 200 million defect images and what compounds

40:54 Closing the loop, and what DeepFix does

43:58 Where the value sits in 2031🔑 Key insights you will walk away with:

➡️ Why rule-based systems needed tens of thousands of images and still only handled a narrow set of checks

➡️ How transformers cut training to under 200 images and 45 minutes, and why that unlocked scale

➡️ The decision to train on good product rather than defects, and what it saves a customer who cannot stop the line

➡️ Why plants in remote locations and confidential product lines made cloud inference a non starter

➡️ What actually goes wrong on deployment, starting with teams who disagree on what good looks like

➡️ Why a pilot dies if value does not land in weeks rather than months

➡️ The founder advice on assessing willingness to pay before building

➡️ Why the hardware versus software split matters less than revenue quality and pricing power

➡️ How 200 million real defect images compound into something synthetic data cannot replicate

➡️ The case for special purpose systems over general purpose humanoids on a factory lineLinks:

Aniruddha Banerjee: https://www.linkedin.com/in/anirudhabanerjee/

SwitchOn: https://switchon.io/

Rohit Yadav: https://www.linkedin.com/in/rohityadav23/

Newsletter: https://yadavrohit.substack.com/

Rukesh Reddy, founder of Deccan AI, and Purva Bhandari sit down with Rohit Yadav to explain where the data layer sits in the model building stack, and why it does not disappear as models get smarter. Every model has three ingredients: compute, the engineers writing the algorithms, and the data. Deccan builds the third one, supplying training and eval data to the handful of labs building frontier models, and now also helping enterprises deploy AI.

Rukesh came to this from finance, working for a bank in New York before what he calls a complete U-turn. The thesis was a shift in what annotation means. The industry scaled around 2014 with self driving, when the work was bounding boxes, accurate but not IQ heavy. Post ChatGPT it needed engineers, PhDs and domain experts who can reason with a model, which he saw as a right to win for a company delivering out of India. The name is deliberate too: after learning that Cisco is a homage to San Francisco, he asked why nobody was claiming the Deccan, India’s own corridor across Hyderabad, Bangalore and Pune. The company bootstrapped for 18 months, raised a small round, then a Series A in March.

Whether you build models, deploy them inside an enterprise, or invest in either, this is a clear look at the layer most people underestimate.

🔑 Key insights you will walk away with:

➡️ Why the data layer survives every change in algorithm, from RLHF through to RL

➡️ The difference between full stack and first party models, and why those lines keep blurring

➡️ Why in house data teams at labs are a shrinking share of the market

➡️ How synthetic and human data fused into a hybrid, and why one or two percent of human input is the kicker

➡️ Why knowing where models break is harder than generating data, and what that demands of a platform

➡️ The case for starting in one country when cycle times can be as short as a day

➡️ Why today’s models have high bookish knowledge and struggle with the exceptions outside codified process

➡️ Why the binding constraint on open source adoption is engineering capacity rather than licensing

➡️ Why ARR is a shaky measure when revenue is far more volatile than previous tech waves

➡️ Why language is a flattened out view of the world, and what world models are actually for

Links:

Rukesh Reddy: https://www.linkedin.com/in/rukeshreddy/

Purva Bhandari: https://www.linkedin.com/in/purvabhandari/

Deccan AI: https://www.deccan.ai/

Rohit Yadav: https://www.linkedin.com/in/rohityadav23/

Newsletter: https://yadavrohit.substack.com/

Anand Jain, founder and CEO of Aerem, sits down with Rohit Yadav to explain why rooftop solar took off almost everywhere except India, and what it took to fix the gap. The economics were never the problem. An MSME pays 8 to 12 rupees a unit and a commercial user 12 to 18, while rooftop solar costs around 3 to 3.5. Payback lands between six months and three years, followed by roughly 25 years of effectively free power. The question Anand kept asking was why a market that obvious was not moving.

Aerem is a full stack platform for distributed solar, combining financing through its own NBFC, B2B commerce, and solar tech. Around 600 crores disbursed, roughly 1,000 crores of annual commerce sales, close to 5,000 installers on the platform, and about 3,500 plants built totalling 1,500 megawatts. Anand brings 25 years to it: Delhi School of Economics, six years in Mumbai across asset management and bank treasury, an MBA at Yale, Wall Street through the financial crisis, then two years in energy at Barclays Capital. He returned to India in 2009 to build a 10 megawatt project under Gujarat's first solar policy, set up KPMG's solar practice, and deployed about a billion dollars in utility scale before starting Aerem five years ago.

Whether you invest in energy, lend against assets, or build in hard physical markets, this one is about why a slam dunk sat untouched for a decade.

Chapters:

00:00 What Aerem is, and the three businesses inside it

05:35 From Delhi School of Economics to Wall Street to Indian solar

14:08 Utility scale versus rooftop, and why both will coexist

19:56 Rooftop was 15 percent of installed capacity. Globally it is 60 to 70.

21:49 Net metering, DISCOMs, and what actually slows adoption down

30:31 The three business lines and how they feed each other

36:09 Why he deliberately avoided the subsidised segment

42:24 Inside the lending book: collateral free, asset financed

45:24 The EPC channel as origination engine, and 100 plus quality checkpoints

49:19 Monitoring data, underwriting, and what it unlocks next

54:14 The competitive map: banks, subsidised lenders, private NBFCs

58:53 Fundraising, and why capital follows a scalable business

1:01:37 What went wrong in US residential solar finance

1:05:54 The next 20 years, and 96 percent of the market still untouched🔑 Key insights you will walk away with:

➡️ Why the savings case for Indian MSMEs is 30 to 80 percent off the power bill, and why that still was not enough on its own

➡️ How rooftop went from 15 percent of installed capacity to around 25, and where he thinks it lands

➡️ Why rooftop obviates transmission and distribution infrastructure entirely, even at a 15 to 20 percent cost premium

➡️ The deliberate decision to build where there is no subsidy, and what subsidies do to market quality

➡️ Why installing a plant costs three to four times more elsewhere, and what higher radiation does to the comparison

➡️ Why lending alone is shooting in the dark when execution takes two to three months

➡️ How 100 plus quality checkpoints during execution feed directly back into underwriting

➡️ Why term loans are collateral free with the financed asset as the only security

➡️ What went wrong in US residential solar: savings that were missold, and tenor mismatches

➡️ Why only 3 to 4 percent of the MSME opportunity has been solarised, in a segment he sizes at 500 billion dollarsLinks:

Anand Jain: https://www.linkedin.com/in/jainanand/

Aerem: https://www.aerem.co/

Rohit Yadav: https://www.linkedin.com/in/rohityadav23/

Newsletter: https://yadavrohit.substack.com/