The founder walked into the meeting believing his product was on trial. It was not. His evidence was.
He had spent three days rehearsing the pitch. The buyer's associate had spent three days reading the customer contracts, the cap table, the training-data licences and the metric definitions in the last two board packs. By the end of the first hour the founder had answered every product question well and every contract question badly, and the price the company would eventually accept was already set.
A buyer does not pay for revenue and technology. It pays for confidence that the revenue will hold, the product will scale, the assets will transfer and nothing ugly will surface after completion. Diligence is where that confidence is won or lost, and most companies run it exactly the wrong way round.
Most treat diligence as a data-room exercise that starts when a sale starts. By the time the exercise starts, the things that cost the most have already happened. The contractor who wrote something important and never signed anything is gone. The training data with no licence has been in the model for eighteen months. The largest customer's change-of-control clause is still in the contract nobody looked at. Fixing any of those inside a process is a specific indemnity, a bigger escrow, a deferred payment or a lower price. Fixing them a year earlier is a signature.
The Ortent Diligence Radar was built for the founder who wants to run it the other way round.
It scores a SaaS or AI company on nine domains that decide how diligence goes: chain of title, data provenance, revenue quality, commercial and change of control, privacy, security and resilience, AI governance and claims, sector and regulatory perimeter, insurability and disclosure. Each domain carries two scores. Exposure is how much the domain can move a deal for this company in particular. A company selling AI into a regulated market carries high exposure on data, sector and AI claims. A horizontal tool with no personal data carries less. Exposure is not a criticism. It is a reading of where the money and the risk actually sit.
Readiness is how well the company can prove its position today, from evidence that already exists. Not evidence you would assemble if asked. Evidence you already keep. A written policy is not readiness. A signed assignment, a reconciled metric, a dated test result, a licence that covers the use, that is readiness.
The gap is exposure minus readiness. The point of the radar is to find the domains where the gap is widest and close them while it is still cheap.
A buyer does not pay for revenue and technology. It pays for confidence.
The urgency around this went up sharply in 2026. Anthropic settled the Bartz training-data copyright class action for around 1.5 billion dollars, roughly 3,000 dollars a book, with final approval in July. That single number turned a theoretical worry about data provenance into a figure a buyer can put in a model. A licensing market now exists as the clean alternative to argument. A buyer testing your data will not accept a fair-use theory where a licence was available and you did not take it. Data provenance moved from an interesting question to a priced one.
The change stacks. Insurers have begun ending what they call silent AI, writing AI-specific exclusions into cyber, technology errors and omissions, directors and officers, and warranty and indemnity cover. If your AI governance, your data rights and your security cannot be evidenced, the insurer carves out those representations at the sale, and the risk they would have carried falls back onto you in a larger escrow, a specific indemnity or a lower headline price. Readiness is the difference between a risk someone else insures and a risk you keep.
The radar has three readers.
The founder or CEO of a SaaS or AI company gets to see the gaps before a buyer does. If you are still years from a process, the gaps are cheap to close. If you are already in one, the same reading tells you where the negotiation is going to concentrate and where to concede time to fix instead of price to accept.
The board or NED gets a governance instrument that reads the quiet domains. Every board tracks revenue. Far fewer track whether the company can prove it owns its IP, has the rights to its data, or can evidence the AI claims in its own marketing. Those are the domains that surface late and cost most, and a board that has never asked to see the evidence is trusting that it exists.
The PE operating partner gets a portfolio lens. The work that closes these gaps is the same work that makes a company better run. A portfolio company that is diligence-ready from the start carries less risk, sells faster, and defends a higher price. That is a return, not an overhead.
None of that is a compliance frame. It is a commercial one. The radar scores nine domains, sets your exposure against your readiness, and shows you the domain most likely to cost enterprise value for a company like yours. There is no gate, no email, no login. The report generates in the browser and downloads free. The only thing the tool wants is that you do the work.
Score your company at ortent.co/tools/diligence-radar. The whitepaper behind it is the argument. The self-scoring prompt is the deep version from your own evidence. And if you want a board-ready read before a raise or a sale, ortent.co/contact.