Multimodal redaction pipeline as a stateless Rust library.
A workspace of library crates hosts (a SaaS backend, a Tauri app, a
language SDK, a custom pipeline) embed directly. No long-running
process, no HTTP layer of its own. Inference is delegated over HTTP to
model services that live in the sibling
nvisycom/bento repository; the
engine ships with a git-dep client (elide-bento) that speaks their
wire contract, and any service reproducing that contract is a drop-in
replacement.
Warning
Active development: API not stable. This project is under active development. Public APIs, configuration shapes, and wire schemas may change without notice between releases. Pin a specific commit if you depend on this in production.
- Multimodal codecs: read, edit, and write PDF, DOCX, images, audio, CSV, JSON, and plain text through a unified span-based content model
- Layered detection: regex, dictionary, and checksum patterns run first at low cost; NER, OCR, VLM, and LLM classification handle what deterministic methods cannot
- Context-aware redaction: mask, replace, hash, encrypt, blur, block, and pixelate with policy-driven rules scoped to entity type, document class, and confidence threshold
- Stateless engine: no persistence, no HTTP layer, no background tasks; every analyze and apply call is self-contained
- Bring your own inference: any service that reproduces the wire contract is a drop-in replacement for the shipped bento services, including self-hosted or custom models and weights
The fastest way to get started is with Nvisy Cloud.
For self-hosted use, embed the engine crate directly and deploy the inference services from nvisycom/bento as sidecar containers. See each crate README for details.
See CHANGELOG.md for release notes and version history.
Apache 2.0 License, see LICENSE.txt
- Documentation: docs.nvisy.com
- Issues: GitHub Issues
- Email: support@nvisy.com