The Future Proof Festival draws its crowds to Huntington Beach every September with promises of the next big disruption — the app, the algorithm, the AI copilot that will supposedly reinvent how advisors run their practices. So it was a little disarming to sit down with Patric Glassell, Chief Growth Officer at Kwanti, and hear him make the case for going slower.
Kwanti has spent nearly two decades building portfolio analytics tools that help advisors win prospects, manage books of business, and generate proposals. It is, by any measure, a fintech company. And yet Glassell spent most of our conversation talking not about what AI can do, but about what it shouldn't be trusted to do — at least not yet. In an industry currently intoxicated by its own AI product announcements, that restraint is worth paying attention to.
Confused People Don't Buy
Start with the basics: what actually happens in a prospect meeting today that couldn't happen a decade ago? Glassell's answer wasn't about dashboards or automation. It was about a phrase he says he repeats constantly.
"I say this line 10 times a week: 'Confused people don't buy,'" he told me. "So what do you want to do? You want to make sure that whoever you're pitching, whoever you're talking to, whether it be a prospect or an existing client, that they understand."
That sounds obvious until you consider what's changed underneath it. Prospects walking into a first meeting are no longer blank slates. Glassell pointed out that advisors now have to assume a prospect has already run their own numbers through ChatGPT or Claude before they ever sit down, poking around for holes in whatever pitch they're about to hear. Rather than treating that as a threat, Kwanti treats it as a bar that's been raised.
"It levels up the advisor's work," he said. "Which is great, because now they actually have to really explain why their expertise is so valuable, which we firmly believe is very, very needed."
The practical upshot is flexibility in how advisors present themselves. Some clients want the two-page summary — performance, risk, done. Others want to see all 55 pages of data, because that's how they're wired to trust something. Glassell cited a stat that stuck with me: LPL reportedly identifies roughly 15 distinct advisor archetypes within its own network, each with different prospecting and book-management styles. Multiply that across the industry and "one-size-fits-all" software starts to look like a losing bet. The value of a good prospecting tool, in his framing, isn't the data itself — it's giving an advisor the ability to answer an unexpected question on the fly, in the room, without fumbling. That's the moment trust either gets built or doesn't.
The Case for Moving Slowly on Purpose
Given how loudly the rest of the industry is shouting about AI right now, I asked Glassell what it actually looks like, day to day, to build a product roadmap with deliberate caution baked in.
He didn't hedge. "It's very easy to be seduced by the shiny objects, and the shiny objects are always coming," he said, adding a memorable description of the current moment: "almost volcanic." He's been on both sides of that seduction — as a former venture investor and as the founder of a company that, in his words, did well for two years and then stagnated. That history seems to inform Kwanti's posture now more than any competitive pressure does.
Part of the confidence comes from tenure. Kwanti has been operating for 18 years, long enough to have lived through Web3 hype, robo-advisor panic, and a handful of other "this changes everything" cycles that mostly didn't. "We've seen the turns," he said. "So we're allowed to take a bit of a cautious approach because we don't need to react quickly." He noted that Kwanti's highest churn comes from advisors retiring — not from clients getting poached by a flashier competitor. That's a meaningfully different risk profile than most fintech startups are working with, and it buys Kwanti room to watch before it leaps.
None of this means Kwanti is ignoring AI. Glassell mentioned the company is using it on the back end for engineering work and demoed an integration with Claude for Financial Advisors at the T3 conference earlier this year. But the emphasis, repeatedly, came back to one word: trust. "The average consumer still has a trust fear with Anthropic, OpenAI, Gemini, and so forth," he said, arguing that Kwanti's 18 years of advisors trusting its numbers is a asset worth protecting rather than rushing to layer new, less-proven technology on top of.
Where AI Should Never Touch the Relationship
If there's a line Kwanti won't cross, Glassell drew it clearly: the client relationship itself.
"Anything with a relationship, the AI should stay as far away from as possible," he said. "That's a human-to-human interaction that clients should want. These aren't robo-advisors. You're trusting a human being to make the best decisions for your financial future."
That principle extends into how Kwanti decides what to build at all. Rather than deciding internally what AI features to ship and pushing them out, Glassell described a more consultative process — asking users and prospects directly what they'd actually want AI to do for them. The answer, he said, has often been underwhelming for anyone hoping for a dramatic pitch: "That's cool, but I don't need it. Not now." Advisors, in his telling, are telling Kwanti to keep experimenting rather than demanding a rollout.
For advisors evaluating any AI-branded tool, Glassell's warning about trust is worth sitting with. "Trust can be built up over 10 years and eroded in 10 seconds if you give them wrong numbers," he said. His prescription isn't to avoid AI, but to verify relentlessly — "check and double check and triple check and quadruple check" — because, as he put it dryly, "we don't see a need to innovate math just yet."
Showing Clients the Ugly Truth
The clearest embodiment of that philosophy is Kwanti's new Cash Flow Scenarios feature, which lets advisors run historical simulations of how a portfolio would have actually performed under different withdrawal or contribution strategies — dividend withdrawals, fixed-dollar withdrawals, percentage-based withdrawals, or fixed contributions, adjusted for inflation if needed.
The design choice was deliberate: back-tested reality instead of projected fantasy. "Everything we do within Kwanti, besides one or two forward-looking income estimates, is backward tested," Glassell explained. "We're not a predictor of the future. We don't recommend stocks. We don't recommend anything. We give the advisor the intelligence and the tools they need to make their best choices."
He described the tool solving a very ordinary meeting problem: a client floats a hypothetical — "What if I contributed $1,000 a month?" or "What if I just withdrew the dividend instead?" — and instead of a hand-wavy answer, the advisor can show, using real market history, what would have actually happened, including the scenarios where a strategy would have failed. Kwanti doesn't soften that. If a withdrawal rate would have drained the account, the simulation shows it.
Is that transparency-first approach connected to the same caution Glassell described around AI, or is it a separate philosophy? He sees them as related but distinct. "That's not AI. That's pure data points," he said. "It's all math. It's all data that they can go back on." The caution around AI is about an unproven trust relationship; the confidence in Cash Flow Scenarios comes from math that's been reliable for millennia. As he put it: "We think [AI] has the potential to be very revolutionary. But anyone that says it's a silver bullet... they're wrong in my view."
For advisors sorting through the noise at a festival built entirely around what's next, that might be the most useful takeaway of all — not a new feature, but a filter for evaluating every new feature that comes after it.
For more information on Kwanti, visit thier website here.
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