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, Co-Founder & CEO of 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 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
