Fitfinity

Before you pay a premium for an AI company, check what it actually owns. A PE firm asked us to look at an acquisition before close. The company had $4.2M ARR, growing 40% year over year, and wanted 12x revenue. Management had 14 slides explaining its “foundational model architecture.” We opened the codebase. GPT-4o, a system prompt, a React frontend and roughly 600 lines connecting them. The proprietary model described in the deck wasn't there. The buyer walked. A company built on OpenAI can still be a valuable business. Customers, distribution, data and software embedded in daily operations all matter. But you need to know which of those you're paying for. In my view, AI diligence needs to answer four questions before close: [1] Model dependency. If OpenAI, Anthropic or Google changes pricing or retires a model, what happens to the product's quality and margins? How much work would it take to move? Ask the team to demonstrate an alternative against the same customer tasks and quality checks. Being able to switch models is a strength. The next question is what customers would still pay this company for if every competitor had access to the same model. That might be years of integration work, a useful dataset or a workflow customers depend on. Get specific. [2] Training data liability. “Proprietary data” needs more explanation than a line in the deck. Where did it come from? What do the licenses and customer agreements allow? Is it used for training, fine-tuning or retrieving information when someone asks a question? These are different uses. Permission for one doesn't automatically cover the others. Have those rights checked against what the product actually does and what you intend to do with it after the acquisition. [3] Talent concentration. Headcount tells you very little about this. Ask someone other than the original builder to deploy the product, investigate a failed run and explain how they test a change before it reaches customers. If every answer requires the same person, you need to understand that dependency before close. Documentation, shared access and a second engineer who can run the system are basic hygiene. Put the work to close those gaps into the acquisition plan. [4] Integration failure rates. A product working for its existing customers doesn't establish what it will take to work inside your portfolio companies. They have different systems, data, permissions and people responsible for approving actions. Pick one intended workflow. Have engineers inspect the systems it needs to connect to, identify the changes and estimate the work. Name the person who will own it and define the outcome you expect. Do that before counting the savings. The financial model can show you ARR, growth and expected synergies. Someone still needs to check the assumptions against the software. Get engineers with hands on the keyboard into the engagement during exclusivity. By close, you should know what you own, what you depend on and what it will cost to deliver the outcomes in the investment case.