Pocket LLM

Pocket LLM is an AI search tool. ThirdAI's PocketLLM is a locally stored document search engine: load PDFs and scraped URLs, ask semantic questions across the corpus, summarize the top results, and teach the model your own jargon. Files and trained models stay on your device, which suits legal, research and internal knowledge base work.

PocketLLM from ThirdAI is a personal knowledge search engine that runs entirely on your own machine. You load thousands of pages of PDFs and documents, point it at URLs to scrape, and then search across the whole corpus with semantic queries rather than exact keyword matching. The pitch is privacy through locality: files and trained models stay on the device, and only you have access to them. Nothing is described as leaving the laptop, which makes the tool relevant to people handling confidential case notes, internal reports or unpublished research. The search layer goes beyond lookup. Users can ask questions about anything inside a document or across an entire set of documents, and because matching is semantic, a full paragraph, a loosely related concept or several topics can be queried in one pass. Results can be narrowed by picking the top three or five hits and having them summarized into a shorter answer. There is also a feedback loop: you can like a result or teach new associations so the model adjusts to your own vocabulary, jargon and phrasing over time. ThirdAI positions PocketLLM for legal teams and journalists assembling past case files into a fast knowledge base, for researchers exploring papers and citing sources quickly, and for anyone who wants an internal document knowledge base for retrieval. The quantitative claims on the page are about speed and scale: training a model up to a billion parameters on a laptop in minutes, with instant search afterwards, plus the ability to add, update or delete models and files at will. How it stacks up against cloud document chat tools is mostly a question of tradeoffs. Hosted competitors usually bring shared workspaces, browser access and team permissions, but they require uploading sensitive material. PocketLLM trades that off for local storage and a single-user model. The site does not publish a feature breakdown for formats, languages or team collaboration, so evaluate fit against your own corpus before committing.