DocMind specializes in one thing the AI industry desperately needs: reducing the token footprint of documents before they reach a language model — without losing any meaning.
A token is the basic unit of text that a large language model (LLM) processes. Think of it as a chunk of text — roughly 3–4 characters on average, or about ¾ of a word in English.
When you send text to a modern AI model, it doesn't read the text as you see it. It first converts the text into a sequence of tokens using a process called tokenization. A sentence like:
The total number of tokens processed — both what you send (input) and what the model generates (output) — determines exactly how much you pay.
AI costs are split into two components:
Everything you send to the model: the document, your instructions, conversation history, examples. This is the largest cost driver for document-heavy applications.
The text the model generates in response: summaries, answers, analysis. Output tokens are typically 2–5× more expensive per token than input tokens.
For document-intensive applications — legal review, due diligence, compliance checking, customer support — input tokens dominate the cost. A 50-page document may require 1 million input tokens just to process a single user query across your knowledge base.
A large language model is a neural network trained on vast amounts of text data. Through this training, it learns statistical patterns — which tokens tend to follow which other tokens — and uses these patterns to predict and generate text.
The key insight: every token in the input is processed by every attention layer in the model. Processing cost scales quadratically with sequence length in the attention mechanism. Fewer tokens → dramatically faster and cheaper processing.
Most documents contain enormous amounts of text that adds no value to AI reasoning: repeated headers and footers, formatting artifacts, boilerplate legal language, embedded image descriptions converted to OCR noise, redundant cross-references, and structural whitespace.
DocMind's compression pipeline — covered by multiple patents pending — strips this noise while preserving the semantic content the AI actually needs. The result is a radically smaller token footprint with equivalent or better AI output quality.
Illustrative scenarios based on published AI model list prices. Results are estimates only. Actual savings vary significantly by document type, content density, model selection, and usage pattern. No results are guaranteed.
Retrieval-Augmented Generation (RAG) is the technique used to give AI models access to your documents without loading everything into a single context window. A retrieval system finds the most relevant chunks of your documents, and only those chunks are sent to the LLM.
The quality of RAG output depends critically on the quality of the chunks. If your document contains noise — OCR artifacts, repeated headers, garbled table text — those noisy chunks pollute the retrieved context. The AI reasons on garbage.
DocMind's core technologies are protected by multiple patent applications covering novel methods in document compression, cryptographic verification, and AI-optimized document processing.
DocMind's compressed output is model-agnostic. The token-optimized text works as context, RAG input, or direct prompt material for any major language model — including leading commercial and open-source options.
Free for up to 3 documents. See the token reduction on your own documents before committing to a plan.