According to PYMNTS.com, Safebooks, an AI-powered data integrity platform, has raised a $15 million seed funding round led by Yahal Zilka’s firm 10D. The company’s core product is an Agentic Revenue Integrity (ARI) layer that continuously monitors and reconciles data across a company’s CRM, ERP, and billing systems, aiming to auto-remediate discrepancies. CEO and co-founder Ahikam Kaufman stated that finance teams spend most of their time on this manual data integrity work. The platform, which works on top of existing infrastructure, is already used by enterprise SaaS companies and has monitored over $40 billion in transactions since launch. The funding announcement was made in a press release on Tuesday, December 9.
The Unsexy Problem Everyone Has
Here’s the thing: what Safebooks is targeting is arguably one of the most universal, tedious, and costly problems in any business of scale. Data living in different silos that should match but never quite do. It’s the spreadsheet hell of manually comparing Salesforce to NetSuite to your billing system, chasing down why the numbers are off by 0.2%. Kaufman is right—teams waste thousands of hours on this. So the premise is solid. If you can truly automate this reconciliation, you’re not just saving time; you’re creating a single source of financial truth that operates in real-time. That’s powerful. In a world where business moves fast, waiting until month-end to discover a revenue leak is a recipe for disaster.
AI as the Glue, Not the Replacement
The interesting angle here is that Safebooks isn’t selling a rip-and-replace system. They’re selling a layer that sits on top of your existing, often messy, tech stack. That’s smart from a sales perspective—lower barrier to entry. But it also defines their entire technical challenge. The AI isn’t just generating reports; it’s supposedly understanding “how financial data, structured and unstructured, connects” across disparate systems. That’s a tall order. It requires the AI to grasp context, document formats, and business logic unique to each company. I think the real test won’t be catching obvious errors, but intelligently handling the edge cases and exceptions that currently require a human’s institutional knowledge. Can an AI truly understand that “ACME Corp (Q4 Renewal)” in an email is the same as “Acme Corporation – Contract ID 789” in the CRM? That’s the hard part.
Skepticism and the Scale Challenge
Now, let’s pump the brakes a little. The space of “automating the back office” is littered with startups that promised to eliminate manual work, only to find that enterprise financial data is a fractal nightmare of complexity. Every company has its own bizarre quirks, legacy systems, and “temporary” workarounds that became permanent. Implementing any system that touches financial data is a massive undertaking, fraught with compliance and audit concerns. Safebooks says it’s already monitoring $40B in transactions, which is a strong start, but scaling that trust across hundreds of large enterprises is another ballgame. And let’s talk about that $15 million seed round. That’s a huge seed. It shows investor conviction, sure, but it also sets sky-high expectations for immediate growth and execution. The pressure is on.
The Broader CFO AI Landscape
Yahal Zilka from 10D isn’t just betting on a tool; he’s betting on a shift. He calls it “the foundational infrastructure” for the AI-transformed office of the CFO. And he might be onto something. The PYMNTS report they cite, a collab with Bottomline and FIS, notes that 70% of CFOs are already using AI for cash flow management. The trend is real. But this also means Safebooks won’t have the field to themselves for long. Every major ERP vendor and a dozen other startups are racing to solve similar problems. Their head start and focused platform—detailed in their vision for Agentic Revenue Integrity—is their advantage. But in the relentless world of enterprise sales, a good technical solution is only half the battle. The other half is convincing risk-averse finance VPs to let an AI autonomously “auto-remediate” their most critical data. That’s a cultural sell as much as a technological one.
