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Actionable
Takeaways

Design your GTM motion around the actual decision your buyer is making — not just the product you're selling:

Advisors joining Savvy aren't buying software. They're becoming 1099 contractors on Savvy's registered investment advisor, migrating their book of business, and repapering client contracts. That's an entirely different sales motion than SaaS. Ritik built a hybrid sales-recruiting team — SDR sourcers paired with account executives running a consultative process — specifically because the decision resembles a career move more than a software purchase. If your product creates this level of lock-in or life disruption for the buyer, your GTM team structure, process, and ICP targeting need to reflect that reality.

Use publicly available compliance data as a targeting signal:

Financial advisors are required to register their licenses with the SEC, making their firm history, tenure, and professional movement publicly searchable. Savvy built a "likelihood to move" model that ingests this data alongside LinkedIn signals to generate a probability score for each prospect. The model is intentionally a black box — they can't fully articulate every variable or its weight — but it lets the sales team concentrate effort on the advisors most structurally ready to make a move, rather than spraying outreach across the market.

Build a dedicated experimentation budget and treat it as a non-negotiable line item:

Ritik runs a formal experimentation allocation that varies from 10% to 50% of the marketing budget in a given quarter. The goal is early signal across a wide diversity of channels — not optimization of existing ones. When a signal fires, the philosophy is simple: 100x spend on that strategy immediately. The repeatable lab — the ability to run cheap, fast, diverse experiments — is the actual GTM asset, not any individual channel.

In trust-dependent markets, earned credibility compounds faster than any outbound motion:

Cold email and conference booths both underdelivered for Savvy. Cold email failed for a structural reason specific to their ICP: financial advisors are heavily solicited, so standing out requires being at the 97th or 98th percentile of quality — and even then it's unclear if it moves the needle. What actually drives growth is advisor-to-advisor word of mouth and partner referrals from others in the wealth management ecosystem who interact with advisors regularly. In markets where the buyer is moving their livelihood, no sequence substitutes for peer trust.

Hire from adjacent industries when the sales motion transfers, even if the domain doesn't:

Rather than defaulting to wealth management veterans, Ritik recruited from industries with structurally similar sales dynamics — insurance brokers, real estate agents — where reps were already experienced at convincing someone to move their entire business. He layered in advisors with financial advisor recruiting backgrounds for domain nuance. The result is a team that cross-pollinates tactics rather than inheriting the assumptions of a single industry.

Don't rebuild infrastructure from scratch just because you can — partner first, build incrementally:

Watching Brex build a banking core from the ground up gave Ritik a clear counterpoint. At Savvy, he took the inverse approach: partner aggressively at the start, absorb the limitations, and build toward better financial infrastructure over time as the business proves itself. For most early-stage founders, going to bare metal is a costly bet on a future scale that isn't guaranteed yet.

The "AI replaces the human" thesis breaks down wherever psychology is the actual product:

Robo-advisors made the same displacement argument in the 2010s. The data showed minimal impact on advisor headcount — and at the lower end of the client wealth spectrum, clients who started with robo advisors eventually graduated to human ones. Ritik's argument is that AI framed purely as a work-output replacement misses why high-net-worth individuals actually hire advisors: as net worth grows, so does decision paralysis and fear of costly mistakes. The psychological function of an advisor — giving clients confidence to act — isn't something an AI agent replicates. The correct framing is AI eliminating middle and back-office friction so advisors maximize time in front of clients, acting as editors of AI-generated work rather than writers of it.

Conversation
Highlights

How Savvy Wealth Built a GTM Motion for a Market Where Trust Is the Product

Most B2B sales processes ask a buyer to change a tool. Savvy Wealth asks something categorically different: move your entire business.

When an independent financial advisor joins Savvy’s platform, they become a 1099 contractor on Savvy’s registered investment advisor, migrate their book of clients, and repaper every client contract in the process. There’s no pilot period, no low-stakes trial. It’s closer to a career decision than a purchasing one.

That single constraint — the weight of what the buyer is actually agreeing to — is the lens through which Ritik Malhotra, Founder and CEO of Savvy Wealth, has designed every part of his go-to-market. In a recent episode of BUILDERS, he broke down the mechanics.

The Sales Motion Has to Match the Decision

Because advisors are moving their entire professional infrastructure, Savvy built a hybrid team that functions part sales, part recruiting — SDR sourcers identifying candidates, account executives running a consultative process that looks more like business due diligence than a demo cycle.

“The actual process itself is highly educative in the sense that we’re effectively treating it like a business consultant,” Ritik said, “where we analyze all parts of their business — where they’re having pain points, where they have gaps — and try to retrofit and see if Savvy’s platform can actually go and fill and help those gaps.”

The implication for founders: if your product creates genuine switching costs or life disruption for the buyer, your team structure, your process, and your ICP targeting all need to reflect the actual magnitude of that decision — not the motion you inherited from a prior company.

Using Public Regulatory Data as a Targeting Signal

Before any conversation happens, Savvy runs a predictive model to determine which advisors are structurally ready to make a move.

The inputs are largely public. Financial advisors in the US are required to register their licenses with the SEC, making their firm history, tenure, and professional movement searchable. Savvy ingests this alongside LinkedIn data to generate a probability score for each prospect.

Ritik was precise about the model’s limitations: “There’s a number of factors, but that’s where effectively we have this somewhat black box model that’s effectively just running a number of permutations across a number of variables. The thing with a black box model is that you can only estimate what those signals are, what the variables are, and what the weights on each of those are.”

That honesty matters. The model doesn’t deliver certainty — it delivers prioritization. It lets a lean sales team concentrate effort on the highest-probability targets rather than working a broad list that moves slowly and trusts carefully. For any founder selling into a regulated ICP, the compliance infrastructure your buyers are required to maintain is often a targeting data layer hiding in plain sight.

Hiring From Adjacent Industries, Not the Target One

When building the sales team, Ritik didn’t default to wealth management veterans. He looked for people who had already sold something requiring a similar level of buyer commitment — the act of convincing someone to move their livelihood.

“Whether it’s insurance brokers or real estate agents,” he said, “that was similar enough because in a similar vein you were asking those individuals to also move their business over.” He layered in people with financial advisor recruiting backgrounds for industry nuance. The blend was intentional: cross-pollinate tactics from adjacent industries rather than inheriting the assumptions — and the ceiling — of a single one.

The Experimentation Framework Behind the GTM Motion

Ritik tried the obvious channels. Cold email underdelivered — financial advisors are heavily solicited, and standing out requires being at a level of quality where the marginal return becomes unclear. Conference booths produced mixed results, with no strong correlation between spend and outcome.

What drives growth: advisor-to-advisor referrals and partners in the wealth management ecosystem who already carry trust with the ICP. In a market where the buyer is moving their livelihood, peer validation compounds faster than any outbound sequence.

His framework for finding what works — and what doesn’t — is structural rather than intuitive. Savvy runs a formal experimentation budget that ranges from 10% to 50% of marketing spend in a given quarter, deliberately allocated to testing a wide diversity of channels.

“As soon as you find the one,” Ritik said, “you basically just 100x your spend on that one strategy. And effectively if you build the best repeatable laboratory of just being able to do this over and over again, that is how you design the best go-to-market motion.”

The lab — the repeatable ability to generate and kill hypotheses fast — is the durable asset. Not any individual channel.

The Infrastructure Lesson From Brex

Before founding Savvy, Ritik spent roughly two and a half years at Brex, where he watched the company scale from approximately $5 million to $100 million in revenue in a single year. He left with two principles.

The first: hire exceptional people. A Brex founder put it plainly — “You get no extra points for doing things yourself, so you might as well get all the help you can.”

The second was a deliberate inversion of what he observed. Brex built its banking core from the ground up. Ritik saw the cost of that bet and took the opposite approach at Savvy.

“When we started Savvy, I actually thought it doesn’t always make sense in principle from a business strategy perspective to go and just rebuild those things. We actually partnered a ton in the beginning and said, look, we can figure out all the limitations as we go.”

The principle: going to bare metal is a bet on future scale that the business hasn’t yet earned the right to make. Partner first, prove the model, build toward better infrastructure incrementally.

Why the AI Displacement Argument Keeps Getting the Value Prop Wrong

Every company in this space has to answer the same question: will AI replace the advisor? Ritik’s answer is grounded in a prior cycle.

Robo-advisors made an identical displacement claim in the 2010s. “The data speaks for itself,” he said. “There’s very little impact to financial advisors.” At the lower end of the wealth spectrum, clients who started with robo-advisors eventually graduated to human ones.

His argument for why this pattern repeats goes beyond market inertia. As net worth grows, so does the psychological complexity of financial decision-making. “There’s additional latent value in an advisor that doesn’t get talked about often — the human nature of someone dealing with their own money actually has a lot of psychological elements that need to be considered.”

Decision paralysis increases with wealth. Fear of making the wrong call — even when the probability distribution favors a decision — is real and persistent. That function isn’t automated away by a better financial planning output.

Savvy’s thesis runs in the opposite direction: use AI to eliminate middle and back-office friction so advisors maximize time in front of clients. “Easier to be an editor than it is to be a writer,” Ritik said. The end state he’s building toward is a team of proactive AI agents — investment analysts, financial planners — surfacing insights before the advisor needs to ask.

One platform. Every money-related decision. The advisor spending their time on the thing only they can do.

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