Nirat Keswani

CEO @ Procense. On a mission to improve the way process manufacturers make the products we use every day. 2x Founder.

[Nirat Keswani @niratkeswani]

Process manufacturers make the goods you use everyday. From soap, to vitamins, food and chemicals. Most still run on paper.

We're fixing that at Procense, and we're hiring:

Senior Full Stack Engineer
Founding AE
Implementation Manager
Customer Success

DM me if interested!

[Nirat Keswani @niratkeswani]

👀

[The All-In Podcast @theallinpod]

Ben Horowitz: “Anthropic is one of the most negligent companies that we've ever seen in terms of security.”

“I'll tell you a funny story. So when we first got Opus 4.5, we were doing cybersecurity checks for the firm.

So we had an agent basically set up a website for a pet store.

And then we took another agent and said, ‘Tell us if we can exploit that web store.’

And it gave us all the exploits it could find.

So Opus 4.5 wrote really buggy code. Great.

And then we said, ‘Oh, the SQL injection, is that actually exploitable that you found?’

And it goes, ‘Let's pop it open.’

And the agent using Opus 4.5 breaks into the website and steals all the customer data immediately.

We then call Anthropic, and what they tell us is, ‘You prompted it wrong.’

Like that was their answer.”

[Nirat Keswani @niratkeswani]

Systems of record won’t die, unless vendors let them.

If you sell a system of record, your job is to make it usable by AI.

Agents need context. They need data, business rules, and the ability to take action. They need to know what happened, what’s happening, and what should happen next.

That context lives in the system of record.

[Nirat Keswani @niratkeswani]

@typesfast Would love to join. Building an AI operations platform and IOT sensors for process manufacturers.

[Nirat Keswani @niratkeswani]

Timing is everything in startups. The DOT compliance mandate gave Samsara the tailwind to walk into a market full of incumbents and take share.

But timing alone doesn’t win markets. Samsara built a genuinely different product: hardware and software as one platform, not two vendors bolted together. That’s what let the data actually come together instead of living in silos.

That’s the part that gets missed. IoT stopped being a point solution and became the context layer for the whole enterprise.

[Nirat Keswani @niratkeswani]

@typesfast DM’d!

[Nirat Keswani @niratkeswani]

@businessbarista Ai

[Nirat Keswani @niratkeswani]

@aalicbarker Interested

[Nirat Keswani @niratkeswani]

@thesamparr And no one’s ever heard of these manufacturers. Gold mine.

[Nirat Keswani @niratkeswani]

Vertical systems have a data moat and will allow you to build better agents, making it harder for someone new to enter your market. Break that down further to the individual customer level, each customer with their own nuances, and your value will continue to compound.

[TBPN @tbpn]

Box CEO @levie's defense of software over vibe-coded, n-of-1 internal tools:

"If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time."

"The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely."

"The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization."

"Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?"

"Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?"

"I think on the margin the average enterprise is going to spend their time and energy on the latter."

"I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."

[Todd Saunders @toddsaunders]

There is so much buy / investment side demand for vertical saas right now.

3 founders I coach all coincidentally closed rounds this month, and 1 just sold to PE (to be announced next week).

1/ raised $2 on $20m doing $1M in ARR
2/ raised $9 on $52M doing $6.5M in ARR / 110% NRR
3/ raised $11M on a $105M doing $8M in ARR / 95% GRR / 135% NRR
4/ sold for $90M, 8.5x ARR

If you are in vertical saas, now is your time.

[Nirat Keswani @niratkeswani]

A vendor sits across the table. A partner sits on the same side.

[Gokul Rajaram @gokulr]

VERTICAL AI CHALLENGE

Vertical AI Founders: You've spent 2+ years building your agents, training your model on your customers' data, embedding into workflows, creating a powerful GTM motion, all the best practices. You've beaten back challengers and are the #1 or #2 player in your vertical.

I'm sorry, you cannot relax. In fact, you need to massively up your game.

Turns out you are facing an existential challenge: long-horizon agents (eg: Claude Code). Agents that are not trained on a specific domain, but can reliably work for hours or days on end in pursuit of a goal, self-correct, and actually do stuff.

I'm sure many Vertical AI founders will say: "Oh, we are not worried. We are the system of record for decision traces. We train on enterprise-specific context. That's why these horizontal agents can never catch up with this."

You might well be right.

But, but, but ... you cannot afford to bury your head in the sand. These long-horizon agents will get better very, very quickly. You need to understand precisely how good they are at the exact jobs you've built your agents on. You cannot wait for someone else to do this. For example, if you're a legal AI company with an agent that automates contract review, you must compare how good your specialized agent is versus a general-purpose long-horizon agent that's simply given the contract and asked to perform the same review.

My challenge to you: Assign a strong engineer on your team to focus 100% on using long-horizon agents (with minimal context, other than just the contract in the example above) to compete with your custom-trained agents. Benchmark how the long-horizon agents perform vs your agent. Rinse and repeat it every few months.

Like with most other things worth measuring, what matters is the rate of improvement (the "slope" vs the Y-intercept). If the long-horizon agent is 30% as good as your vertical agent on Day 1, but 50% as good on Day 60, and 70% as good on Day 120, you need to reassess your product strategy.

AGI is coming for everyone. Long-horizon agents are the closest we have to AGI, and as a Vertical AI company, you need to figure out how you compete and survive.

Game on.

[Nirat Keswani @niratkeswani]

@MartinGTobias Fast time to value is key. If it’s more sales led than product led, we like to add success criteria that triggers the conversion

[Nirat Keswani @niratkeswani]

Accept the risk, test to failure, iterate, repeat, get it right. Expose the weaknesses early. This is how it’s done.

[Palmer Luckey @PalmerLuckey]

We aren't going to change. We aren't going to slow down. That is exactly what they want us to do, exactly what our competitors want, and the opposite of what building safe, powerful, cost-effecfive weapons actually looks like.

[Nirat Keswani @niratkeswani]

@mrsnhl And you keep the equity….

[Craig Weiss @craigweiss]

Somehow this is still not common knowledge, but the best engineers work at high-growth, early stage startups.

This is where you want to be and where talent density is highest. It's never been FAANG.