Intent and Quality: How Jay Singh Builds AI That Wins with Singles and Doubles

We’re back with another edition of Just Curious, where we blend insights from recent interviews with our experts’ own reflections on how AI is being applied in the real world.

This week’s feature: Jay Singh, CEO of Casper Studios, a product studio helping hedge funds, private equity firms, and enterprise leaders build modular AI systems that drive adoption and lasting value.

Before founding Casper, Jay spent five years at LinkedIn leading partnerships across trust and verification. Today, he and his team design modular AI systems that embed into real workflows, reducing research time, increasing decision quality, and helping teams use AI, not just talk about it.

Jay has a knack for translating the noise of AI into calm, usable systems. “Our job,” he told me, “is to make AI feel calmer, less overwhelming, so leaders can actually integrate it into how they work every day.” 

That focus on clarity and adoption has become Casper’s edge.

What to expect:

• Why small wins, not moonshots, create lasting AI momentum

• How modular builds help AI systems scale faster

• A hedge fund case study: 80% time reduction in research workflows

• What readiness signals matter more than clean data

• The overlooked power of voice and how to use it to listen to your team

Want to connect with Jay and Casper Studios? Just reply to this message, and I’ll facilitate.

Watch my interview with Jay Singh

👉 Watch here! → 

Expert Q&A: Jay Singh, CEO of Casper Studios

(Each week, we ask our applied AI experts a rotating set of questions to surface their frameworks, lessons, and insights. Jay’s answers follow, with Stu’s thoughts and context from our full conversation.)

1. Describe who you are, what you do, who you do it for, and what makes your approach unique.

I run a company called Casper Studios. We design and build AI products. We work with everyone from billion-dollar hedge funds and PE-backed companies to global brands like Netflix. What makes us different is that we move fast, but with intent and quality.

A lot of firms either “talk strategy” or “ship products.” We do both. We’ve built generative voice activations for entertainment companies, internal research tools for funds, and automation systems that save analysts 10–15 hours a week. We call it applied AI, meaning the work has to work.

We’re a small team, but we’ve all been inside big companies - LinkedIn, Microsoft, Accenture, OpenAI, etc. - and that helps us bridge the corporate mindset with startup speed.

Stu's Thoughts:

Jay’s framing that “applied AI means the work has to work” is simple and grounded. In our conversation, he shared an example of helping hedge funds reduce research prep time by 80% through careful design around workflows.

“Our job,” he said, “is to make AI feel calmer, less overwhelming, so leaders can actually integrate it into how they work every day.”

That balance between speed and intentionality has become Casper’s signature: prototype fast, but never at the expense of usability or adoption.

He credits Casper’s success to a modular design philosophy. “Singles and doubles,” as he calls it. Where each build adds value on its own and compounds over time. “You don’t need a moonshot to prove impact,” he said. “You just need something that works and scales.”

2. What problem are you most focused on solving right now, and how are you anticipating solving this problem with AI?

Right now, I’m focused on helping companies close the gap between AI potential and AI adoption—and do so in a way that’s simple and easily understandable. Everyone’s experimenting, but few are integrating it into real workflows.

We’re solving that by embedding with teams directly, whether it’s a PE firm trying to deploy us to their portcos, or a creative agency wanting to make interactive campaigns using AI. We sit in the middle, build alongside them. We act as your product, eng, and AI teams as a service.

The bigger vision is to build reusable modules for clients to use internally so the marginal cost of their AI investments decreases over time.

Stu's Thoughts:

Jay sees what many executives miss: most AI projects stall not because of poor models, but because of poor adoption design. He described Casper’s “embedded” approach as the bridge between consulting and engineering, literally sitting with teams to co-build systems that stick.

He described his role as helping leaders “experiment without panic.” “It’s a new form of technology,” he told me. “You should act on it fast, but without panic.” That balance between urgency and steadiness defines Casper’s embedded model.

In our interview, he put it this way:

“The middle ground is experimenting, but without panic. It’s okay if you’re not ahead. Just start, and start small.”

That mindset of steady experimentation paired with embedded delivery is what allows Casper to scale practical AI across large organizations without losing momentum.

3. What’s one thing you wish every CEO understood before they invested in AI?

AI won’t fix a messy business. It amplifies what’s already there, good or bad. If your processes are unclear, your data is inconsistent, or your team is not aligned, AI will expose that faster.

The best CEOs use AI themselves. They see where it shines and where it struggles. They unblock their teams. They push IT and security to act as enablers, not gatekeepers.

Stu's Thoughts:

Jay’s point here is that AI doesn’t fix dysfunction but can accelerate it. It’s leverage both ways.

He shared that one of his first questions for any prospective client is whether the executive team personally uses ChatGPT, Claude, or Replit.

“If leadership isn’t using AI themselves,” he said, “that’s a red flag. They’re probably misjudging both its potential and its timeline.” 

AI isn’t magic.

His advice to CEOs: start by using it yourself. Not for optics, but to understand the friction your team faces because adoption is as much cultural as technical.

4. What AI capability is just around the corner that businesses should prepare for now?

Voice is still underrated. Talking is faster, more natural, and it unlocks use cases that typing can’t.

That said, most companies don’t even need cutting-edge tech yet. There’s huge value in just teaching teams to use off-the-shelf AI tools and rolling out simple automations internally.

Stu's Thoughts:

Casper’s team recently built a voice agent to help clients collect internal feedback.

“We had 22 of 30 employees record 10- to 45-minute voice sessions about what’s working, what’s not,” Jay told me. “That data was pure gold.”

He sees voice as a powerful tool for surfacing organizational intelligence, what he calls “Employee Voice 2.0.” In a world full of dashboards and forms, a conversational interface invites honesty and speed.

5. If you had $1M to invest in an AI initiative today, where would you put it?

I do not think it is about where to spend $1M. It’s about why you’re spending it. The better question is: What are the key pain points across the organization that, if automated, could give your team back time and energy? Then make the investment.

I always tell clients to start small. Run a short training. Get your team hands-on. Then, once people are comfortable, work with your leads to quantify ROI before scaling further..

Stu's Thoughts:

Jay inverts the question. “Why spend?” before “Where to spend? It’s a quiet critique of how many enterprises treat AI as a budget line item instead of a capability or a solution to a problem.

“The best relationships start with a small pilot. We prove value, earn trust, and grow from there.”

It’s the same discipline that powered Casper’s hedge fund engagement, where modular builds led to an 80% reduction in research time. The same mindset applies everywhere: pilot first, prove value, then scale.

He compares AI adoption to baseball: “Most people try to hit home runs before they’ve learned to hit singles.” 

Focus on small, repeatable wins that build confidence and internal capability.

Key takeaways:

1. Experiment without panic. Start now, but don’t chase the hype. Educate, test, and integrate steadily.

2. Fix the process before the model. AI amplifies what’s already there. Clean up systems before automating them.

3. Use voice to listen. Before designing AI for customers, use it to better understand your own people.

Connecting with Jay

Here are a couple of options:

• Respond to this newsletter, and we’ll help facilitate (it’s free)

• Check out Jay’s Just Curious profile and connect with him there (also, free)

• Explore Casper Studios: casperstudios.xyz

• Also, don’t forget: Watch our interview → 

Across Jay’s work, one common thread is calm execution. He’s not chasing the next model or hype cycle. He’s focused on designing systems people actually use and get value from. This discipline turns experimentation into leverage, proof that winning with AI won’t be because you’re the loudest but the one who is (quietly) working.