A data room tells you what an AI company earns. You have to actually open the codebase to find out whether it'll keep earning or what it's worth. A PE firm once asked us to look at an M&A target before close. The data room showed $4.2M ARR growing 40% a year, and the deck promised a proprietary AI platform. We looked at the codebase, and it turned out to be a GPT-4o call with a system prompt and about 600 lines of glue code. They were asking 12x revenue, and the firm walked away. These are the seven things we check about every AI target before close: 1. Ask how long a senior engineer would need to rebuild it. If the answer is a weekend, you're paying a software multiple for a sales team. 2. Open the system prompt. Plenty of "proprietary AI features" turn out to be one well-written prompt. Read it yourself before you buy. 3. Ask for the eval set. An eval set is a collection of production cases with answers the company's own experts agreed on. Without one, nobody there can prove the product still works after they change a model or a prompt. 4. Find out who can explain how the AI works. Standard HR diligence counts heads. In an AI company, the knowledge that keeps the product running tends to sit with two or three engineers, and you want their names before you sign. 5. Trace the training data. Bartz v. Anthropic settled at $3,000 per book across 500,000 works, $1.5 billion in total. When you buy an AI company, you inherit however it sourced its data, and EU AI Act violations can reach 7% of global turnover. 6. Put AI costs next to revenue per customer. Ask for inference cost per customer, broken out from the rest of COGS. If heavy users cost more to serve than they pay, growth makes the margin worse. 7. Check what gets logged. Ask to see the traces for one customer request from start to finish. If the company can't produce them, nobody there can explain a bad output to a customer (or a regulator for that matter). Financial and commercial diligence are standard on every deal. Technical diligence on an AI company belongs on that list, because the codebase is where the valuation falls apart. That's what we run for PE firms at Limestone Digital.