Aiva provides a private AI thinking partner that runs locally on a user’s machine, allowing it to analyze documents, images, and research without sending data to external servers. It supports offline operation and optional, explicit web access, enabling organizations to keep all files in‑house while still benefiting from AI‑driven reasoning across multiple file types and evidence sources.
Funding
Funding not disclosed
Founders
Product
Problem
Professionals and organizations need to analyze and synthesize information from documents, images, and research data, but using cloud‑based AI tools risks exposing proprietary or sensitive content to external servers.
Solution
Aiva provides a locally‑run AI assistant that ingests and reasons over a user's full set of files without transmitting that data to third parties. The system operates fully offline by default, with an optional, user‑initiated web lookup that is kept separate from the private knowledge base. By processing on the user's machine or on controlled organizational infrastructure, Aiva delivers answers that are grounded in the complete evidence set while preserving confidentiality. The architecture scales from a single workstation to shared, air‑gapped deployments, maintaining the same privacy‑first model across macOS and Windows platforms.
Target Audience
Primary users are knowledge workers, researchers, and enterprises that handle confidential documents and require AI assistance without compromising data security.
Features
- Local, on‑device inference engine that runs on Apple Silicon and Windows, ensuring data never leaves the host machine
- Explicit, opt‑in web access for current information that is isolated from private source material
- Multi‑modal support for text documents, images, and research files, enabling cross‑file reasoning
- Visible traceability from query to cited evidence, allowing users to review the source of each answer
- Enterprise‑grade deployment option for shared, air‑gapped infrastructure while retaining the same privacy guarantees