Why up to 50% of Savvy Wealth’s marketing budget goes towards experimentation

How Safebooks AI positioned against the 80% accuracy standard that makes AI unacceptable in finance | Ahikam Kaufman

Safebooks AI is building the infrastructure layer that makes agentic AI safe to operate inside the office of the CFO. Where most finance automation tools solve point problems — AP, AR, billing, reconciliation — Safebooks ingests data end to end across every system in a company's financial stack: CPQ, CRM, contract management, billing, ERP, and banking. Using graph AI technology, it normalizes that data into a complete, traversable audit trail so AI agents can process every transaction with the accuracy and completeness that financial compliance actually demands. In a recent episode of BUILDERS, we sat down with ⁠Ahikam Kaufman⁠ [https://www.linkedin.com/in/ahikam-kaufman-688310/], Co-Founder & CEO of ⁠Safebooks AI⁠ [https://safebooks.ai/], to learn how a career inside the office of the CFO — including time at Mercury Interactive and a post-acquisition role at Intuit — led him to build the data infrastructure layer that makes agentic finance real. Topics Discussed: * Why the office of the CFO requires a fundamentally different accuracy standard than any other AI use case — and how Safebooks architected around that constraint from day one * How graph AI technology creates a unified, end-to-end audit trail across structured and unstructured financial systems * The SOC1 certification strategy and customer UAT process Safebooks uses to establish trust with risk-averse finance buyers * Why Ahikam positions around "finance operations automation" rather than "financial data governance" — and the category design logic behind that choice GTM Lessons For B2B Founders: * The accuracy ceiling is your positioning. Most AI go-to-market is built around aggregate improvement metrics — productivity gains, error reduction percentages, time saved. Safebooks identified that this framing actively undermines trust with their specific buyer. As Ahikam put it: "When you run AI for marketing or sales and let's say 80% is correct, then that's good enough. In finance, it's not good enough." He didn't just say this in sales conversations — he built the entire product architecture around it, including the graph AI layer that creates a complete transaction audit trail before any agent touches the data. Founders targeting regulated or high-stakes buyers should pressure-test whether their accuracy positioning is calibrated to their ICP's actual risk tolerance, not to the median SaaS buyer's. If your buyer operates in an environment where partial accuracy creates liability, that ceiling is your sharpest differentiator — lead with it explicitly. * Use compliance certifications as a trust wedge, not a checkbox. Safebooks pursued SOC1 certification — a standard typically associated with financial controls audits, not software products — as an active part of their sales motion with CFO buyers. Paired with customer UAT against their own historical data, this creates a proof path that doesn't require the buyer to take Safebooks' word for anything. The sequence matters: let the prospect run their own validation against data they already know, then back it with a certification framework they already respect. Founders selling into enterprise buyers with established risk and compliance functions should map the specific third-party certifications their buyers already rely on and pursue those proactively, rather than building a trust narrative entirely on case studies. //  Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership.⁠ www.FrontLines.io⁠ [http://www.frontlines.io] The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.⁠ www.GlobalTalent.co⁠ [http://www.globaltalent.co] // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here:⁠ https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM [https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM]

What ToltIQ's co-founder — a former KKR CIO — says founders must never do when selling AI to financial services buyers | Ed Brandman

Ed Brandman⁠ [https://www.linkedin.com/in/ed-brandman] spent decades in global financial services before retiring in 2018. His last chapter before stepping away was at KKR — where he joined when the firm had just 390 people and left having helped build it into one of the most recognizable names in alternative assets. Five years later, a conversation with his son (now his co-founder) about the due diligence process pulled him back. That became ⁠ToltIQ⁠ [https://toltiq.com/], an AI-native platform built specifically for private markets. In this episode of BUILDERS, Ed breaks down a GTM that ran entirely on referrals for two-plus years, how a deliberate industry-first hiring policy replaced a sales team, and what founders consistently get wrong when trying to sell AI to financial services buyers who are already overwhelmed. Topics Discussed: * Why Ed and his co-founder targeted the front end of the investment workflow — not back-office ops — as the highest AI leverage point * The deliberate decision to staff 70% of the team, including engineers, from inside the industry * How ToltIQ generated 8–10 inbounds per week for two years with no outbound motion — and what finally made them add one * Running a 30-person team against a 100-person competitor using AI internally across the entire org * The three things Ed tells every founder trying to sell into financial services CIOs * Why the Frontier model providers (OpenAI, Anthropic) may be the biggest threat founders aren't pricing into their moat GTM Lessons For B2B Founders: * The highest AI leverage in financial services isn't where most founders look. Ed's conviction from the start — drawn directly from his time inside KKR — was that the front end of investment workflows (diligence, capital raising, investor relations, sourcing) would yield far more from AI than operational back-office processes. That's the opposite of where most AI vendors pitch. If you're building for a specialized vertical, time spent inside the industry isn't just helpful for credibility — it's how you identify where the real leverage is before you build anything. * Hire the domain, then train for the tool. 70% of ToltIQ's team — including engineers and the client-facing org — came from inside private markets. Ed's view: if clients can sit across from your team and feel understood before the demo starts, you've already cleared the biggest hurdle in enterprise sales. This wasn't incidental. It was a deliberate hiring philosophy from day one, and it scaled the business before there was a sales playbook. * Referral growth at this scale requires earning it, not engineering it. ToltIQ had no outbound motion for more than two years and was still fielding 8–10 inbounds per week by the end of 2025. Ed's explanation: the time they invested in onboarding clients — working through problems with them, being transparent about limitations, iterating in the open — made clients want to refer peers. In tight-knit professional networks like private markets, the quality of the relationship drives referrals more than the quality of the product alone. The referral engine sustained the company through 2025 and into 2026 before they felt the ceiling. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership.⁠ www.FrontLines.io⁠ [http://www.frontlines.io] The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.⁠ www.GlobalTalent.co⁠ [http://www.globaltalent.co] // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here:⁠ https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM [https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM]

How GradBridge is building distribution through school partnerships to reach students at the point of decline | Jen O'Donald

Every year, more than half of private student loan applicants get declined. Not because they're unserious about their education — but because they narrowly miss a credit cutoff. For upperclassmen and grad students already deep into a degree, that rejection often means dropping out. ⁠Jen O'Donald⁠ [https://www.linkedin.com/in/jenodonald/] spent 13 years at Sallie Mae, most recently running product, watching this gap go unsolved. So she built ⁠GradBridge⁠ [http://www.gradbridge.com/] to solve it — creating an entirely new category in student lending: the second look. In this episode, Jen breaks down what it actually takes to go from zero to live in heavily regulated fintech, how she managed a multi-stakeholder launch across a sponsor bank, servicing platform, and compliance stack, and why federal student loan policy shifts are reshaping the entire private lending market in real time. Topics Discussed: * Why half of private student loan applicants get declined — and what it costs them * How GradBridge identified and defined a category that didn't previously exist * The "circular reference" problem of building in regulated fintech and how to move through it * Coordinating a launch across a sponsor bank, origination platform, servicing platform, and compliance stack * How federal policy changes are shifting private student loan demand — and how GradBridge repositioned in real time * School partnerships and referral channels as the core distribution strategy * What "flawless execution" looks like in a zero-tolerance regulated environment heading into peak season // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership.⁠ www.FrontLines.io⁠ [http://www.frontlines.io] The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.⁠ www.GlobalTalent.co⁠ [http://www.globaltalent.co] // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here:⁠ https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM [https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM]

Why up to 50% of Savvy Wealth’s marketing budget goes towards experimentation

Savvy Wealth⁠ [https://savvywealth.com/] is an AI-enabled platform for independent financial advisors — solo operators and small teams — that handles everything from CRM and billing to compliance, investment management, and financial planning. In this episode of BUILDERS, I sat down with ⁠Ritik Malhotra⁠ [https://www.linkedin.com/in/ritikmalhotra/], Founder & CEO, to get into the GTM mechanics behind selling into one of the most trust-locked markets in financial services: advisors who don't just buy software — they move their entire business. Topics Discussed: * What Ritik took — and deliberately inverted — from watching Brex scale from ~$5M to $100M in revenue in a single year * Why Savvy's GTM motion is structurally closer to recruiting than B2B sales — and what that means for team design * How a data science-driven "likelihood to move" model shapes top-of-funnel targeting * What's actually driving growth: brand trust and advisor word-of-mouth over outbound * Why cold email and conference booths underdelivered, and the experimentation framework Ritik runs instead * How Savvy deliberately blends adjacent-industry sales talent with wealth management insiders * Why the "AI replaces the advisor" framing gets the value prop of human financial guidance fundamentally wrong * The long-term vision: a fully vertically integrated operating system for financial advisors, orchestrated by proactive AI agents // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

How Monet used Facebook groups to sign up 7,500 content creators before building the product

Jacob Casson⁠ [https://www.linkedin.com/in/jakemcasson/] spent years trying to solve cash flow for the entertainment and media industry — influencer agencies, production houses, film and TV — while nearly running his own company into the ground twice. In this episode, he breaks down how ⁠Monet⁠ [https://monet.money] evolved from a creator banking product into a financial back office and lending platform, how he recapitalized under a hostile takeover attempt, and why the UK media industry is one of the most defensible fintech niches nobody is building for. Topics Discussed: * Why traditional lenders systematically misprice influencer agency risk * How Monet ended up inside Coldplay's global marketing payment flows * The pivot from creator-facing banking to agency financial infrastructure * Surviving a hostile takeover attempt and engineering a recapitalization * The decision to stay UK-focused in 2025 and what it would actually take to enter the US * Expanding into film and TV debt: tax credits, pre-sales, and broadcasting license fees * Raising debt vs. equity: why conflating the two is a costly fintech mistake * The founder psychology of performing better under pressure than in calm // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount.⁠ www.FrontLines.io/Podcast-as-a-Service [http://www.frontlines.io/Podcast-as-a-Service]