I've been talking to Controllers for the past two years about AI and automation. And I keep hearing the same thing:
"We'd love to automate more of our revenue accounting processes. But we can't explain it to our auditors."
It's not a technology problem. It's a documentation problem.
Here's what happens when a finance team tries to automate their revenue recognition:
You spend weeks building sophisticated workflows and logics to handle complicated use cases like usage-based pricing, contract modifications, and multi-element arrangements. The automation all works beautifully. You close faster; and the numbers are way more accurate.
Then audit season arrives.
Now you need to document everything. Write SOX narratives. Create process flows. Explain the logic. Map the controls. By the time you're done, it's been weeks—and the documentation is already outdated because you might have updated your workflows twice since you started writing.
And here's the real killer: every time you update a workflow going forward, your SOX change management controls require you to manually update the documentation too. Miss that step once - just once - and you've created a control deficiency. Your documentation doesn't match your actual process, and that's an auditor's red flag.
So you have three options, all bad:
Most Controllers I talk to are stuck in Option 2, wishing they could get to Option 3 but can't justify the cost for just their revenue processes.
Now add AI to the mix.
AI can design brilliant automated workflows. It can handle edge cases you didn't even know existed. It can adapt to new contract types without manual reprogramming.
But if you can't explain exactly how the workflows are designed and planned, you can't use it in production. Not for revenue recognition. Not for anything that touches SOX controls.
The better the AI, the harder it is to audit. That's the paradox that's been blocking AI adoption in finance.
We decided to solve this at the root: the AI system has to document itself.
Luca now automatically generates audit-ready documentation for every automation workflow it creates. Plain-English SOX narratives, process logic explanations, data source mappings - generated simultaneously with the rule itself instantaneously, not weeks later.
This isn't a summary. It's a technical narrative that explains:
All in language your auditor can actually understand.
Your SOX narratives stay current automatically. When you update a workflow with Luca, the documentation updates. When you add a new revenue stream, the explanation generates. You stop preparing for audits and start being permanently audit-ready.
The biggest fear about AI in finance is the black box problem. You can't audit what you can't explain. Auto-documentation solves this. Luca designs the rule, our deterministic engine executes it, and both get documented automatically. The black box becomes a glass box.
Right now, your most complex revenue rules exist in the head of one senior accountant. When they leave, that knowledge leaves with them. With auto-documentation, the logic is embedded in the system. New hires read the narrative and understand immediately how your revenue recognition works.
Your best people stop spending weeks writing process docs and start solving actual revenue recognition challenges. But more importantly, you've eliminated a common source of control failures: humans forgetting to update documentation when they update workflows.
Manual change management processes rely on discipline and memory. Automated change management is built into the system. That's not just efficiency - that's fundamentally stronger controls.
For the first time, you can build sophisticated revenue accounting automation without increasing inherent audit risk.
Usage-based pricing? Document it automatically.
Complex contract modifications? Documented.
Multi-element arrangements? Documented.
Refund and reserve accounting policies? Documented.
The more complex your revenue model, the more valuable this becomes.
We're not trying to replace your entire SOX program. We're solving the specific pain point that's blocking finance teams from automating their most complex processes: revenue recognition.
This is how you move from "interesting AI pilot" to "critical infrastructure."
Finance is moving toward a world where automation and compliance aren't opposing forces. Where using AI in accounting logic doesn't offset efficiency with documentation debt. Where AI transparency isn't optional.
The systems we build should generate their own proof. That's not a feature. That's the baseline.
Anything less is just shifting the burden from one manual process to another.