Occlira

Occlira solves a problem that shows up the moment a company starts using LLMs seriously: the useful documents are full of names, addresses, account numbers and recordings you are not allowed to paste into a cloud model. It detects and redacts personally identifiable information across documents, audio and images, and it does the processing 100% locally on your machine, so the sensitive version never leaves the device. The redaction uses reversible placeholders rather than black boxes, which is the detail that makes it practical — you can send the anonymized text to a model, get a result back, and then restore the real values in your own environment. Coverage is broader than a text find-and-replace. It handles scanned PDFs through OCR, detects PII inside audio with speaker diarization so you know who said what, finds it in images, and scrubs file metadata that quietly carries identity and location data. Seven languages are supported, which matters for teams operating across regions where a purely English detection model would silently miss things. Typical users are businesses in regulated spaces — legal, healthcare, finance, HR — plus anyone building an AI workflow on top of customer data who needs a defensible answer to "where did this data go?" Honest limits: local processing means it is bound by your own hardware, so large audio or scan batches take real time. Automated PII detection is very good but never perfect, so anything legally consequential still needs a human check. And because nothing is cloud-hosted, there is no shared team dashboard by default. As a preprocessing step in front of any AI tool, it removes the most common reason legal says no.