Before any AI model reads a single customer message, Obsei has already stripped out the names, email addresses, card numbers, and ID numbers hiding inside it. Obsei pulls in what customers are saying from App Store and Google Play reviews, Zendesk, Freshdesk, and Intercom tickets, GitHub issues, surveys, YouTube comments, Bluesky, RSS feeds, SQL databases, and even Gong call transcripts. Every record is pseudonymised on the way in and stored in an encrypted database that never phones home. Then the labelling starts: sentiment, intent, language, and your own custom questions, each answered with a confidence score by a zero-shot classifier running locally on your machine, or by an LLM you choose through your own keys. Answers the model is unsure about are escalated to a stronger model or queued for a human instead of being silently wrong. Related complaints roll up into stable themes with week-by-week trends, and anything mentioned by fewer than five people stays hidden, so individuals cannot be picked out of a chart. When a theme spikes, alerts and digests land in Slack, and individual findings can be routed straight into Jira, Linear, or GitHub issues. Your analysts browse it all in the built-in Studio, and your AI agents can ask questions over MCP and get cited, redacted evidence back, with any action they propose waiting for your approval. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.