Build vs. Buy: The AI Question Every Lender Will Face This Year

We tested the leading AI models against real attorney entity reviews. Here's what we found.

Your dev team can wire up an AI document reviewer in a week. I'm serious. With the models available today, a competent developer can build something that ingests a PDF, pulls key provisions from an operating agreement, and returns a detailed summary. It will impress your team in a demo.

We know because we tried it. At Highland Law, we've done entity reviews for commercial lenders since 1996. Our legal team performs hundreds of legal entity reviews every month. When we tested the leading AI models against our own attorney-prepared reviews, the results weren't close.

Confident-sounding analysis that missed legal nuance. Incorrect assumptions about authority. And not one model could reliably produce a proper signature block — which is the most important output in the entire review. The models weren't broken. They just weren't built for this.

A prototype can extract. Lending teams need analysis.

Today's large language models can read an operating agreement and summarize it. But lending teams don't need summaries. They need answers. Who has signing authority? Can that person actually bind the entity to this loan? What about the amendment from 2019 that quietly added a unanimous member approval requirement for incurring indebtedness? What happens when the Operating Agreement names a manager, but the Articles of Organization state the entity is member-managed? This happens more often than most people realize. And general-purpose AI misses it routinely.

To be clear, AI development tools are genuinely impressive right now, and for plenty of business workflows, building in-house makes sense. We use Claude and other AI tools every day at EntityScan AI and at Highland Law. But entity document review for commercial lending is a vastly different use case. The cost of being wrong isn't an inconvenience. It's litigation risk.

The real problem isn't the code. It's the data.

Every "build vs. buy" discussion in AI tends to focus on infrastructure, model selection, and developer time. Those are real costs. But they're not where custom AI projects fail to pan out.

By most estimates, 60 to 80 percent of the effort in a successful AI project goes into data preparation. For domain-specific legal work, that data needs to be expert-validated — not just raw documents, but completed reviews where an attorney has already determined the correct legal analysis. The correct ownership breakdown. The correct authority chain. The correct signature block.

Where does a lender get thousands of attorney-validated entity reviews? You don't have them. Your legal counsel may have done reviews for you over the years, but that work product is scattered across deal files — memos, emails, markups. Pulling it together and structuring it as training data is a multi-month project that costs six figures before you write a single line of AI code. And you'd still end up with a fraction of what you actually need.

Why this data can't be replicated quickly

EntityScan AI has been validated against 10,000+ attorney-prepared entity reviews, drawing on nearly three decades of commercial lending practice. Each review involved a real borrower entity, a real deal, and a real attorney determining the correct ownership, signing authority, and signature block — the judgment EntityScan's ownership findings, signing-authority conclusions, and suggested signature blocks are compared against.

The patterns and edge cases and exceptions in that dataset reflect the full messiness of entity structures that commercial lenders encounter in practice — the kind of complexity you only learn from doing thousands of these.

The underlying models that power EntityScan AI are available to anyone. The harder foundation to reproduce is the expert validation: thousands of comparisons against attorney-prepared reviews, informed by decades of specialized lending practice.

The math

The annual cost of EntityScan AI is less than what most firms spend in the first few weeks of scoping an internal build. And that doesn't account for the high rate of custom AI projects that never make it to production at all.

Build where it creates a real edge

There are workflows where building your own AI gives you a genuine competitive advantage — proprietary underwriting models, internal deal scoring, portfolio analytics built on data that lives inside your own systems or is readily available. Build those.

The documents are messy, full of edge cases, and vary wildly from deal to deal. EntityScan AI addresses that problem with validation against 10,000+ attorney-prepared entity reviews and three decades of practice.

Spend your build resources where they'll create a competitive advantage. For entity review, the advantage has already been built.

Benchmark updated October 19, 2026: EntityScan has now been validated against 10,000+ attorney-prepared entity reviews.

Update: The Subscription Now Includes More Than the Report

The economics of this decision now include another capability. Every EntityScan report subscription includes unlimited access to the EntityScan Assistant for questions about the report. Teams can get help understanding identified risk factors, working through ways to address them, and determining steps and documents needed when they prefer different signers.

For a lender evaluating alternatives, the comparison therefore extends beyond the cost of producing a report. It also includes the work of developing and maintaining assistance that uses the report's findings to support the next steps. EntityScan makes both available together through the report subscription.

See what's included and how pricing works →