Finance Is Vibecoding Now. Here’s What That Actually Means.
We’ve been using Lumera to ship enterprise AI automations for finance teams. Today we’re opening up access to builders in the finance community. We want you to join us. Sign up for the waitlist here.
If you’d told me two years ago that finance professionals would be among the adopters of AI coding tools, I’d have been skeptical. This is historically one of the most risk-averse, process-driven functions in any organization.
But it actually makes sense when you look at the structure of the work.
Finance sits at the intersection of every system in the company. ERPs, HRISs, CRMs, billing platforms, bank feeds, expense tools. All of it flows through finance eventually. And for decades, the job of connecting these systems has fallen on finance teams themselves, armed with nothing but Excel, VLOOKUPs, and sheer determination.
The “long tail” of finance work, the stuff that doesn’t fit neatly into any vendor’s product, is enormous. Every company has its own chart of accounts structure, its own allocation methodology, its own close process quirks. Standard SaaS tools handle maybe 70% of the work. The remaining 30%? That’s where finance teams have always been left to figure it out on their own.
AI agents are the first technology that actually addresses that 30%. LLMs can parse unstructured data. The Slack message explaining why a vendor invoice is 3x higher than usual. The email thread about a contract amendment. The PDF of a workers’ comp audit. They can apply business logic that’s too nuanced for rules engines. And with the coding capabilities now available, finance professionals can wire these together into tools that actually run.
So they started building. A controller builds a reconciliation agent that matches transactions across three systems. An FP&A analyst creates an automated flux analysis tool that pulls from the GL, compares against budget, and drafts variance explanations. A revenue ops lead wires together an agent that flags at-risk renewals by cross-referencing usage data, support tickets, and payment history.
These aren’t theoretical examples. They’re happening right now.
The problem nobody’s talking about
Here’s where it gets uncomfortable.
Most of these projects live on someone’s laptop. They’re running in individual ChatGPT conversations, local Python scripts, or Claude Code sessions that one person on the team understands.
There’s no audit trail for the decisions the agent makes. No access controls determining who can run it or modify it. No version control. No error handling beyond “it worked when I tested it.” No SOC 2 compliance. No way for internal audit to review what’s happening.
For any other function, this might be manageable. For finance, where every number eventually flows to a financial statement, where SOX compliance isn’t optional, where a single misclassified transaction can cascade into a material weakness, this is a serious problem.
And the gap is only going to widen. The coding tools are getting better every few weeks. Claude Code now has background agents, automatic memories, and plugin ecosystems. Codex is running multi-agent workflows with automations that fire on a schedule. The friction to build something useful is dropping fast. The friction to make it production-ready hasn’t changed at all.
The gap between “it works on my machine” and “it runs our close”
Think about what happened with shadow IT a decade ago. Business teams started adopting SaaS tools without IT’s blessing because the tools were better than what IT was offering. The response wasn’t to ban SaaS. It was to build governance frameworks that enabled safe adoption.
We’re at the same inflection point with AI in finance. Except the stakes are higher. When a finance team deploys an ungoverned AI agent that’s making journal entries or classifying transactions, you’re one bad automation away from a material weakness.
The gap between prototype and production isn’t about making the model smarter. It’s about everything around the model. Audit logging every decision the agent makes. Role-based access controls. Human-in-the-loop approvals for high-stakes actions. Data lineage tracking. Error handling and alerting. Integration security. And the ability for someone who isn’t the original builder to understand what’s happening and why.
This is the problem that keeps me up at night. And it’s the problem we built Lumera to solve.
Introducing the Lumera community
Lumera is the infrastructure layer for finance AI. We’re building the platform that takes your AI agents from prototype to production, with enterprise security, audit trails, and governance built in from Day 1.
But a platform alone isn’t enough. The finance professionals building these tools need each other. They need a place to share what’s working, troubleshoot what isn’t, and collectively figure out what best practices look like for this entirely new category of work.
We just opened early access to the Lumera community.
Here’s what you get as a member:
Full platform access. Build, test, and deploy finance AI agents with production-grade infrastructure underneath. Connectors for your existing systems. A decision engine that logs every action. Observability so you can actually see what your agents are doing.
Hands-on help shipping your first agent. Our team will work with you directly to take your most painful manual process and turn it into a production-ready AI workflow. Reconciliations, journal entries, flux analysis, variance reporting. Whatever’s eating your team’s time.
A community of finance builders who get it. Not a generic AI community. Not a vendor user group. A focused group of finance professionals who are actively building with AI and understand both the technology and the domain.
We’re keeping the first cohort intentionally small. We want to go deep with each member, learn what you’re building, and make sure the platform evolves based on real workflows, not hypothetical use cases.
Who this is for
If you’re a finance professional who’s already experimenting with AI, whether that’s a Python script, a GPT wrapper, or a full agent workflow, and you’ve hit the wall between “cool demo” and “something we can actually rely on.” This is for you.
If you’re a controller or accounting manager who’s seen your team spend days on work that should take hours, and you know there’s a better way but you’re not sure how to get there safely. This is for you.
If you’re an FP&A lead who wants to automate the tedious parts of variance analysis and reporting so your team can focus on actual analysis. This is for you.
If you’ve looked at what these tools can do now, the coding agents, the Excel integrations, the finance-specific skills, and thought “this could transform how we work, if we could just get it to production without creating a compliance nightmare.” This is for you.
Join us
The tools are ready. The question is whether finance teams will adopt them through the front door, with proper governance, audit trails, and security, or through the back door, on someone’s laptop, with no controls at all.
We’re building the infrastructure to make the first option possible. And we want you in the room as we do it.
Join the Lumera community waitlist →
First cohort is limited. If this resonates, don’t wait.
Sowmya is the CEO and co-founder of Lumera, AI infrastructure for finance teams. She was previously Controller at OpenAI and Rippling, and led Corporate Accounting at Square.
The 30% long tail that never gets automated is where most of the real work lives. It's also where errors happen, controls break, and audit trails go dark. Getting that under control matters for every finance team, and by extension, for anyone relying on their numbers.
This is so interesting. As someone that works in finance and been trying to use Claude to make my CRM and analytics easier. Cant wait to check this out