Most teams reacting to AI just rip features out of the UI when things get slow. We refused. If you're not building magical experiences, you're already behind.

If your product takes 2 minutes to load, it’s already obsolete.

When we integrated Materialize, load times dropped from 120 seconds to under one. That’s the gap between frustration and magic.

Customers won’t wait. Neither should your infrastructure.

You can’t scale yourself. Until now.

Delphi turns experts into software. One version of your mind, talking to thousands at once. Context-aware, high-fidelity, always on.

One-to-one is dead. This is one-to-many. And it's just the beginning.

In AI, 'good enough' isn't enough—not anymore. Users are comparing every new product to ChatGPT and expecting that level of fluidity, speed, and clarity. So when our insights dashboard started timing out because of heavy queries, the easy move would have been to remove the complicated features. That’s the path most teams take. But we didn’t. We brought in Materialize, rebuilt how data flows through Delphi, and cut load times by 120x.

Why? Because you don’t deliver magic by cutting corners. You do it by rethinking your infrastructure. If agents and LLMs are the future, your product either works at that level or it doesn't.

When you’re trying to deliver real-time context to LLMs or agents, your infrastructure either delivers or it breaks. No middle ground.

Before we integrated Materialize, loading our core audience views could take up to 2 minutes—and often failed outright. That meant hiding insights, avoiding complexity, or accepting lag as inevitable.

But expectations have shifted. AI-native products don't get a pass on performance. So we rebuilt what sat beneath the surface.

Now that same dataset loads in under a second. Same view. Same complexity. Built on something that can actually handle it.

If you're building anything AI-native, your data infra isn't just a bottleneck—it's often the first thing holding you back from building at all.

Your expertise deserves to scale.

At Delphi, we’re building for people who have something worth teaching. Coaches, founders, operators, researchers—anyone whose knowledge has real-world impact. We give you a digital version of your mind that can interact, adapt, and teach continuously.

But creating AI-native products that meet today’s bar of experience isn’t easy. Status updates that cause systems to break, dashboards that buckle under real-time usage—most infrastructure wasn’t built for this. Ours is.

If your product doesn’t feel magical, your customers notice. They’ve seen what’s possible. They’ve used FANG-level tools.

We’ve learned that the magic isn’t just in the model. It’s in resilient systems, fast feedback loops, and tools like Materialize that make real-time feel effortless.

The future of learning is one-to-many, always-on, and deeply human. And it starts with giving experts superpowers.

If your product relies on complex, fast-changing data, the traditional pipelines won't cut it. We learned that firsthand. Our audience and conversation dashboards were timing out at two minutes. Some even failed to load.

Before Materialize, we were making tough compromises: remove features, simplify views, lower expectations. But users are comparing your UI not against your competitors — but against what they see from OpenAI, Apple, or Google. Anything slower than instant feels broken.

Since integrating Materialize last summer, we’ve seen a 120x improvement in load speeds. We went from two minutes to under a second.

The game-changer came from flipping the model. Instead of re-computing everything every time — which breaks under LLM-scale agent queries — Materialize lets us precompute and incrementally maintain SQL views in real time. The result is dependable, fast, and far more scalable.

If you’re thinking about making your product AI-native, but your infra can’t keep up — this is the bottleneck you’re going to hit. Live context requires live data products. And tools like Materialize make that possible, without rewriting your entire backend.

You don’t need a team of 100 to ship this kind of experience. But you do need to rethink where your compute lives, and how your UX expectations are evolving.

Start with the moment where Dara explains that Delphi lets you create a digital version of your mind. Then cut into the visual of the green dots lighting up with active users, emphasizing that this isn’t a demo, these are real experts being engaged in real time. Show the insights view briefly to give a glimpse of the power behind the curtain. Then pivot into the product challenge — the audience table lagging due to scale — and immediately flip it into the bigger point: legacy systems can’t handle real-time AI expectations anymore. End with Dara breaking down how high the bar has become, and why good isn’t enough. You need something magical.

When you're building AI-native products, there's no more room for just 'good enough.' Expectations are skyrocketing. Users compare your product to the best of what Big Tech offers—and nothing less. So when we saw our dashboard experiences timing out or lagging because of complexity, we didn't simplify the product just to make it load. We rebuilt the infrastructure to support the magic. That’s why bringing in a system like Materialize wasn’t just a tech decision—it was a promise to our users that speed, scale, and trust are nonnegotiable.

What if you could create a version of your mind that teaches others while you sleep? This clip opens on the future of knowledge sharing: experts turning themselves into scalable digital entities. Then we cut to a real-time shot of the dashboard packed with live dots — each one a person actively learning from a digital version of them. It contrasts this progress with the backend challenges, slowdowns, and how most infrastructure can’t keep up. Then we hit the transition to a bold statement: the bar is rising fast. AI’s not a nice-to-have anymore — you have to be magical or you lose. This is the moment that connects creator ambition with technical innovation.

Most AI products feel fine. Deli has to feel magical. When users are seeing what the top tech companies are delivering, good isn't enough. Expectations are skyrocketing and if your infrastructure can't keep up, you're already behind. We saw load times drop from 2 minutes to under a second after adopting the right approach. That's the bar now.

What happens when your product breaks under real-world scale? Before Materialize, loading a data-rich view took up to 2 minutes. Sometimes it failed altogether. Now it loads in under a second. That’s not minor optimization. That’s what modern users expect. The teams that meet this bar aren’t removing features to survive scale, they’re embracing tools that let you deliver magic—even with a small team.

Imagine if your knowledge could talk to thousands of people at once. Delphi lets experts build a digital version of their mind—literally. Not some gimmicky avatar, but a fully functional, AI-native product that scales your thinking from one-to-one to one-to-many. Watch this moment where Dara breaks down why your digital self needs to keep up with a world where ‘good enough’ products just don’t cut it anymore.

Everyone’s building AI products right now. But here’s the problem: users don’t want just good anymore. They’re expecting magic. And if your data infrastructure can’t handle the complexity of real-time AI, you’re not building magic, you’re building frustration. At Delphi, we hit that wall—two-minute load times, UI breaking, audience insights disappearing. Then we integrated Materialize and everything changed. What used to crash now loads in under a second. This is what it takes to build AI that feels like the future, not 2010.

Imagine opening a page and waiting 2 minutes... or it just crashes. That’s what was happening with our audience overview before we brought in Materialize. Once we made the switch, we dropped load times from 2 minutes to under a second. At scale. That kind of jump isn’t just performance—it’s survival if you're serving real-time AI experiences. Most infrastructure just can’t handle what LLMs and agents need in live context. If you're building anything AI-native, this is the shift to pay attention to.