Start with the work, not the model.
Our interest begins with a specific workflow. Who does the work today? Where does it slow down, become expensive, or produce inconsistent results? An AI business becomes easier to understand when its product has a clear place in that workflow.
We want to understand the customer’s starting point before discussing the technology. A useful comparison includes the existing process, the proposed improvement, and the cost of making the change.
Look at the whole delivery system.
The model is one part of a product. Data access, integrations, evaluation, user experience, and oversight can all shape whether a solution becomes useful. Our investment lens considers how these elements work together.
We ask founders how they would know the product is failing, what happens when an output is wrong, and how the customer stays in control. These questions help make a product’s practical boundaries visible.
Ask what becomes stronger with use.
We are interested in the sources of long-term differentiation: a deeper understanding of a workflow, trusted distribution, thoughtful product design, or access to information that can be used responsibly.
The question is not simply whether an AI product can be built. It is whether the company can keep earning its place in a customer’s business as technology and alternatives change.
Follow the economics of a completed task.
A subscription price says little about the economics of serving a customer on its own. We want to understand what happens between the first request and a result the customer can actually use. Model usage, human review, integrations, support, and the handling of exceptions can all affect the cost of delivery.
Consider an illustrative document-processing product. Extracting a field correctly is one step. Resolving ambiguous documents, asking for missing information, and placing the result into the customer’s system may be the work that determines whether the product saves time. An evaluation should follow the full task rather than stopping at the model output.
Distinguish a pilot from a buying decision.
A pilot can demonstrate that something is technically possible. A buying decision also depends on who owns the budget, who approves the change, what the deployment requires, and how the customer will judge success after the initial enthusiasm has passed.
Useful evidence might include repeated use by the intended team, a defined route from evaluation to paid deployment, or a clear explanation of why a customer stopped using the product. Each helps identify whether the obstacle is the product, the implementation, the price, or the problem itself.
Test the boundaries of the advantage.
Our discussion also considers what could weaken the business. What happens if the underlying model becomes cheaper or a general-purpose platform adds a similar feature? Which parts of the customer relationship would remain valuable?
We look for a clear answer about where the company earns its place: distinctive workflow knowledge, reliable execution, thoughtful integration, trusted distribution, or another advantage that customers recognize. The strongest explanation connects technical capability to a reason the customer will continue to choose the business.
- What measurable customer problem does the product address?
- What must be true for a customer to adopt it?
- Which advantage can the business strengthen over time?
An expression of Ooventive’s investment perspective, not a statement about a specific investment or portfolio company.
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