Governance Ops

How We Built AI for Legal Entity Management

July 15, 2025

by

Sean Di Somma

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Artificial intelligence is everywhere these days, yet almost nowhere near governance operations. While other departments experiment with generative tools, legal, tax and finance teams are struggling to manage increasing complexity, rising risk and shrinking resources. The pressure is real and it’s only growing.

Governance operations sits right at the center of that pressure. It’s not glamorous, but it is essential. Every compliance obligation, cross-border transaction and audit trail ties back to accurate, up-to-date entity data. That makes it a smart, strategic place to start applying AI… as long as it’s done right.

The challenge is that most tools weren’t built for this world. They’re generic, they’re risky, and they don’t address the real burden: the meticulous, time-consuming, high-stakes nature of governance operations. So what do legal teams actually need from AI, and where do most tools fall short?

What AI Can Do Today

Today’s best implementations don’t try to replace legal expertise. They support it. They handle the repetitive, error-prone parts of the workflow and bring forward the information legal teams actually need. 

That’s how we approached building AI into our platform: not as a standalone solution, but as an embedded layer of intelligence that meets governance ops professionals right inside their workflow. The AI built into our governance operations platform already helps with the parts of the job that eat up hours:

  • Document intake and tagging: Upload a signed resolution, and AI can detect what type of document it is, match it to the correct entity and suggest the appropriate tags.

  • Director and officer updates: Instead of manually entering the same information across multiple records, AI can extract names, roles and dates from documents and auto-populate those fields so they’re ready for human verification.

  • Security and ownership structure: AI can interpret complex capitalization tables or shareholder agreements and pre-fill key fields for review.

  • Template-based entity creation: Based on uploaded documents or selected templates, AI can draft the skeleton of a new entity record, pulling in relevant jurisdictional data and standard clauses.

None of this is magic. It’s simply AI doing what it does best: recognizing patterns, extracting structured data and speeding up the most mundane parts of an otherwise high-stakes job.

What a Day with AI Looks Like

You upload a signed shareholder agreement. Instead of starting from scratch, the platform flags it as an equity-related filing, pulls the ownership percentages and suggests updates to the cap table. You review, tweak a line and approve. Two minutes later, your entity record is current and compliant, with a full audit trail.

Or maybe you’re onboarding five new entities at once. AI reads the incorporation docs, fills in the standard data fields and leaves a clear trail of what it interpreted, so you can verify or override before finalizing anything.

These are the kinds of tasks that previously took hours of careful, manual entry. Now, they’re done in minutes, with built-in checkpoints that keep humans in the loop and in control.

Governance Ops Is Evolving, So Should the Tools Supporting It

Entity management has always required rigor. In today’s business environment, it also demands scale.

AI isn’t a silver bullet. It won’t replace legal thinking or eliminate complexity, but when built intentionally and embedded inside systems designed for governance ops, it has the power to shift the burden off overextended teams and onto smart, automated processes.

The goal isn’t to chase trends. It’s to build trust, maintain control and get more done without burning out the people doing the work. As entity structures grow and compliance demands evolve, the teams that thrive will be the ones who embrace purpose-built AI, not as a shortcut, but as a smarter way to work.

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