Redpill Linpro CX

Marketers and CRM consultants paste customer data into AI chats every day. Emails, personnummer, customer IDs, names inside SQL literals. Each paste is a governance incident waiting to happen, and manual redaction is slow and error-prone. Clipboard Firewall is a macOS menu bar app that sanitizes your clipboard on-device, before the data can leave. Copy a customer export, a support ticket, or an SFMC query; by the time you paste, PII is replaced with consistent pseudonyms ({{PERSON_1}}, {{CUSTOMER_ID_1}}), while code, SQL schema, and public information stay untouched. Clean text passes through silently. One click restores the original. Under the hood: a hybrid detector. A deterministic regex layer with checksum validation handles fixed formats (email, phone, IBAN, personnummer, credit card). A fine-tuned Gemma 4 E4B, running fully locally via Ollama, handles what patterns cannot: names in mixed Swedish/English text, coreference ("Anna Lindqvist", "Anna", "she" map to one pseudonym), quasi-identifiers ("our only customer in Kiruna over 90"), and customer IDs that depend on context. The model only detects; verified code performs every replacement, so it can never rewrite your text. Built during the hackathon: the full app, a 190-example synthetic dataset, and a LoRA fine-tune (trained on a cloud GPU, merged to GGUF, shipped on-device). On the frozen held-out set, customer ID recall went from 69% to 100% (13 of 13) with zero false positives on clean text and 100% valid JSON. The failed training attempts and fixes are documented in the repo. No cloud calls, no telemetry, one endpoint: localhost. Turn off Wi-Fi and it keeps working, because nothing it does ever needed the internet.