Search should be
a solved problem.

Every company accumulates knowledge. Too little of it is easy to use.

A customer asks a question your documentation already answers. An engineer repeats work because the original decision is buried in a document. An AI assistant has access to a powerful model but cannot find the policy that would make its answer useful.

The information exists. Getting it to the person or system that needs it remains an engineering project.

That cost compounds.

Teams spend time preparing data, connecting services, tuning retrieval, and keeping the whole thing running. Every team that takes this on spends engineering time before its users see a useful result. Smaller teams often narrow what they build to fit what they can maintain.

As software takes on more work, this limitation matters more. An agent reviewing a contract needs the relevant clause. A support assistant needs the current return policy. The value of a capable model depends in part on the information it can reach.

We think access to that information should be something a developer can add to a product and rely on.

That is the company we are building.

WonderSearch makes your files searchable through MCP, API, CLI, and SDK. You can ask in natural language without building an embedding pipeline or managing search servers.

We chose a simple operating model: search compute is charged per query, and storage is billed separately. A collection can be worth keeping even when it is searched infrequently. The economics should make that practical.

Ease of integration is only the beginning. Search has to return useful material, preserve specific details, and give people a way back to the source. We publish benchmarks so you can inspect our progress. Your own questions and data should decide whether we have earned your business.

A small team should be able to build something ambitious.

We want a developer to try an idea that depends on searching a large collection without first planning a retrieval infrastructure project. We want a business to make its accumulated knowledge useful wherever work happens. We want better search to expand what people can build.

Getting there requires serious work on our side. The product should make that work disappear from yours.

You build what comes next.
We’ll build the search.