Take on bigger AI contracts
without taking on unlimited AI risk

Yes. Ollive’s insurance structure is designed around the AI vendor as the insured, with coverage supporting defined contractual indemnity and defence obligations owed to an enterprise customer when a covered AI failure triggers them.

Insurance does not make an unlimited contractual obligation disappear.

Ollive helps turn an open-ended liability discussion into a defined amount of risk that can be underwritten and financially backed. If the customer requires a higher limit, that becomes a commercial and underwriting decision rather than an undefined exposure.

Ollive also has developed an AI Liability Calculator tool to help you and your enterprise customer align on the liability of your AI deployment

The Assurance Certificate is the buyer-facing evidence that a defined indemnity is backed by insurance.

It identifies the AI vendor as the insured, the enterprise customer relying on the indemnity, and the amount of protection supporting that relationship.

For procurement and legal teams, it answers a more useful question than “do you have insurance?”:

Is the AI liability you have agreed to take on for us actually financially backed?

Ollive’s insurance architecture deliberately uses the Assurance Certificate and Enterprise Indemnity structure rather than making every enterprise customer an Additional Insured.

Your AI company is the insured.

Ollive operates as the MGA, owning the underwriting logic, risk taxonomy, runtime measurement and policyholder relationship. A licensed carrier or specialty capacity partner provides the insurance balance sheet.

Your enterprise customer is protected through the contractual indemnity you have agreed to provide, rather than becoming the insured under your policy.

The premium base is the forward 12-month revenue generated by the AI services being insured, not the number of logs your agents produce.

Ollive then uses production evidence to differentiate the risk: the agent’s role and autonomy, interaction volume, jurisdiction, AI revenue concentration, verified controls, production behavior and how quickly the system is changing can all affect the underwriting view.

Two companies with the same revenue can therefore present very different AI risk.

No waiting period is built into the underwriting model.

Ollive uses the historical logs and traces your agents already generate as underwriting information. When that history is sufficient, the risk can be experience-rated from measured behavior. Newer deployments with less history can be class-rated where appropriate.

What does matter is instrumentation: Ollive needs enough production evidence to understand and continue monitoring the agents being insured.

Not automatically.

Insurability depends on what the agent does, the authority it has, who can be harmed by its actions, the jurisdictions it operates in, the controls around it and whether its behavior can be observed well enough to underwrite.

Ollive is built primarily around customer-facing voice, chat and workflow agents where third-party liability can be defined and measured. Some risks fall outside the current coverage architecture rather than being priced at any cost.

The question from customers in discovery has consistently been some version of: where are the boundaries, and how do you determine the amount of risk? Ollive’s answer is to underwrite the actual role and production behavior rather than assume every AI deployment is equivalent.

Start with the deal.

Ollive looks at the agent involved, the contractual liability you are being asked to accept, the requested limit, the economics of the account and the production evidence available for that deployment.

The vendor remains insured, the relevant enterprise relationship can be reflected in the insurance structure, and the customer can receive an Assurance Certificate evidencing the protection behind the agreed indemnity.

This is why Ollive is most useful during the contract negotiation, not months after the deal has already stalled. Customer discovery has repeatedly shown that the real buying trigger is a valuable account asking the vendor to carry more liability than it is comfortable retaining.

The same production evidence used to monitor and underwrite the agent creates a record of what happened: what the agent saw, what it said or did, which model and prompt version were active, what authority it had and which controls were operating.

That record can support the investigation of whether the event falls within the policy and the insured’s contractual obligation.

The claim itself is determined under the policy wording; Ollive’s role is to make sure the underlying AI behavior does not have to be reconstructed from scratch after the fact.

Usually when the insurance protects more enterprise value than it costs.

Discovery has shown that founders will often negotiate a liability request down first. Insurance becomes much more compelling when the customer is large, strategically important or unwilling to compromise, and accepting the exposure or losing the account are both worse outcomes.

That also creates a commercial option: if one customer is demanding materially higher protection than your standard contract provides, the incremental cost of that protection can potentially be reflected in the deal rather than absorbed across your entire business.