Similarix

Similarix is a semantic search layer for image assets stored in S3. It indexes bucket contents read-only, then supports text queries and image-to-image matching, duplicate detection and API access, with in-house models and support for 133 languages. Plans are tiered by the number of files processed per month.

Similarix adds an AI indexing layer on top of object storage so that image libraries become searchable by meaning rather than by filename. S3 itself has no real search capability, and Similarix is built explicitly around that gap: it reads your buckets, builds indexes from the visual and textual content of each asset, and returns matches for natural language queries or for a sample image you supply. Access to the originals stays read-only, so the tool never modifies or deletes the files sitting in your storage. In practice a team points Similarix at one or more S3 collections (or uploads files directly through the interface or the Upload API, for those who would rather not configure a bucket first). Indexing runs asynchronously and finishes in roughly a minute or two for small collections, stretching to a few hours for libraries holding thousands of files. A dashboard notification confirms when the index is ready. From that point, users search by text or by example image, review near duplicates and exact duplicates, and call the same capability through the API, which is included on the Pro tier. The product targets developers and operations teams who already keep media in S3 (product photography, field imagery, internal archives) and want retrieval without migrating to a dedicated digital asset management system. That positioning matters when weighing alternatives: where a DAM typically owns your files and bundles metadata workflows, branding tools and review cycles, Similarix deliberately stays thin and leaves storage where it is. Coverage currently centers on image formats, with PDFs, documents and spreadsheets promised for a later release. Text queries work across 133 languages, and the underlying models are developed in-house, so there is no dependency on external providers. Billing scales with the number of assets processed and stored per month, and the headline plans list fixed processing allowances that buyers should compare against their real library size. Larger operations can request custom models trained on their own assets and redistribution rights under an enterprise agreement.