Local-first search: retrieval for sensitive data
A paper published on June 30, 2026 proposes a framework for privacy-focused information retrieval, with indexes, models, and inference on users’ devices.
Source: As We May Search: uma abordagem local-first para recuperação de informação (arxiv.org). Text prepared with AI from this source.
What happened and what to do
The paper “As We May Search,” published on June 30, 2026, proposes a local-first approach to information retrieval. Indexes, models, and inference run on users’ devices, while remote services remain optional. It presents a framework for organizing retrieval architecture decisions, particularly for settings involving sensitive data.
A company can assess which documents and queries should stay on-device, prototype local search, and define when remote services may be needed. Implementation could include an exposure diagnosis, architecture design, APIs, and monitoring to track privacy and operation.
How the consultancy can help
Wendelmaques can diagnose data exposure and search requirements, propose a local-first architecture, and implement the necessary components with a defined scope for operation and monitoring.
Next step
Send a short description of your data, users, and search needs to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.