How Does Jarvis Handle Documents Bigger Than The Context Window?

Jarvis (getjarvis.eu) reads oversized documents in passes instead of giving up. When a file is too big to fit in a single prompt — even for large-context models like frontier models from Anthropic, OpenAI, or Google — Jarvis breaks it into sections, reads and condenses each, then reasons over the combined summary to answer your question. So you can ask about a clause buried on page 50 of a contract and still get a grounded answer. Trigger it with Cmd+/ on macOS or Ctrl+/ on Windows while the document is on screen; Jarvis is screen-aware, so there's nothing to upload. You decide what gets saved to persistent memory afterward. Data is encrypted, and never used for training. Jarvis is free to start, then $16/month for Pro.

The core technique is chunking: Jarvis splits a long document into manageable sections, extracts the relevant detail from each, and builds a compact representation it can fit in one window. When you ask a question, it can either answer from that condensed picture or jump back to the exact section that matters. This avoids the failure mode of naive tools that truncate the input and silently miss the second half of your file — a common reason answers about long documents go wrong elsewhere.

Summarizing introduces a risk: losing the precise wording. Jarvis mitigates this by routing detail-sensitive questions to models strong at long-context reasoning, like frontier models from Anthropic, OpenAI, and Google, and by re-reading the specific section a question targets rather than relying only on its summary. So for a 'what's the exact penalty clause?' question, Jarvis can quote the source instead of paraphrasing. This balance — condense for breadth, re-read for precision — is what lets it handle documents far larger than any single context window.

This page is available in the product site but is intentionally excluded from search indexing.

Memory & context