Named to the 2026 Forbes Fintech 50, Maybern is the system of record for private funds. We're building category-defining software for private funds. Our mission: empower the office of the modern Fund CFO to shift from operational validator to strategic leader. We encode fund logic into a structured, computable data model. Every number is auditable, versioned, and traceable. When fund data is structured and correct by design, you can build on top of it, enabling new insights not possible across spreadsheets.
External link for Maybern
598 Broadway
4th Floor
New York, NY 10012, US
See you Wednesday with our latest and greatest! https://lnkd.in/ef4cFupH
We’re pleased to announce AI-enabled fund finance platform Maybern as a sponsor of the Chronograph 2026 AGM User Conference. Maybern empowers the Office of the Fund CFO with structured fund intelligence — automating complex calculations, enabling scenario modeling, and turning CFOs and their teams into strategic business leaders. 📍 NYC | May 27 🔗 Learn more: https://lnkd.in/eTsvJqvy
On Maybern, the agent operates like every other actor in the close: bound by role-based permissions. Roles are defined at the platform layer: controller, reviewer, approver, observer. Maker-checker is a structural workflow tied to the role and the user, recorded against the specific change. When the agent acts, it acts as the role it was assigned. Same audit trail as a human user.
Three weeks after close, the auditor asks how you got a number. The spreadsheet has been overwritten. This is the replayability gap, and it predates AI. "Audit trail" in most workflows is a folder of email confirmations and a tab labeled "as-of 6/30 FINAL FINAL v3." Replayability has to be built into the infrastructure. It shouldn't be something the team produces under audit pressure.
On Maybern, the same input always produces the same output. Bit for bit, every close. The math runs on a deterministic rules engine. We use AI to draft and validate the rules in that engine: waterfalls, allocations, GP/LP economic splits. The engine runs them every close in code that's traceable and auditable.
Fund math has one rule that doesn't bend: same input, same output, every time. The auditor doesn't accept "approximately." Most software falls short of that bar. Excel drifts between users, custom scripts depend on the engineer who wrote them, and general-purpose AI does great work everywhere except as the system of record for a number.
How will the modern CFO begin to trust middle and back office agents? They will need: 1. Deterministic rails the math runs on. 2. Full replayability of every action: what changed, when, and against which version of the logic. 3. Permissions enforced at the platform layer. Our clients are running this on live fund data today.
The CFOs we work with don't ask about speed. They ask about source. "Show me the cell, the formula, the row in the ledger." Until replayability is built into AI workflows this will always be an issue. We've spent three years building the structured ledger that fixes this. The agent that runs on it doesn't draft a memo. It closes the fund. Shows its work. Answers the LP questions about their tier-3 catch-up.
We're excited to welcome Tripp Smith to Maybern as our SVP of Forward Deployed Engineering. As we continue building the AI-native platform for private fund finance, Tripp will lead the team that brings our AI and data capabilities directly to customers, helping Private Markets Fund Managers move faster from implementation to impact through agentic automation, advanced analytics, and scenario modeling. Tripp has spent his career at the intersection of AI, data, and enterprise transformation, including roles as an Apex Architect and Global Industry CTO at Snowflake and CTO of Clarity Insights through its scale to $100M+ and acquisition. Explore our open roles at the link in the comments. Welcome, Tripp!
Completed my first day at Maybern working with Ashwin Raghu to build the agentic operating model for institutional fund managers: deterministic fund rails, integrated agentic workflows, and Forward Deployed Engineering connecting the fund’s systems, controls, and decisions at enterprise scale. This is the fourth time Ashwin and I have teamed up in our careers to solve hard Data and AI problems. There is no higher honor as a leader than watching a former mentee become the kind of leader you want to work for, and more, for them to relentlessly recruit you when they are in a position to hire anyone. No one knows exactly how AI will reshape the future, but I am certain that relationships, trust, and the way we invest in people will matter more than ever. I am incredibly proud to join the high-performance team Ashwin and Ross Mechanic have built at Maybern and to continue to build with our partners Snowflake and Anthropic. If you thrive on solving challenging Data and AI problems and seeing your work drive business impact, we want you on our team.
AI agents are about to enter the fund close. The architecture has two layers. At build time, AI generates the validated fund logic. Waterfall engines, allocation rules, GP/LP splits. Reviewed once and version-stamped. At run time, deterministic code executes. Same inputs, same outputs, every close. AI agents orchestrate; the rails do the math. Five control layers your auditor already knows how to test: tamper-evident audit trail, determinism, versioning with rollback, platform-level maker/checker, and a governed platform for agents. This is what audit-ready AI in fund finance looks like. The agentic era runs on rails. https://lnkd.in/eXrEJeeg