Coworker brings reasoning-based retrieval to millions of documents—so AI can navigate a body of knowledge, not just search through fragments of it.
Enterprise knowledge is not one long document. It is a living estate of contracts, reports, policies, analyses, technical manuals, and records—often millions deep.
For years, the standard answer has been to cut every file into passages, turn each passage into a vector, and retrieve the nearest matches to a question. It is fast, familiar, and useful. But it carries a quiet assumption: that the nearest-looking text is the most useful evidence.
Split documents into uniform passages, whether or not their meaning ends there.
Represent each passage as a fixed-length numerical signature.
Return the top matches that resemble the wording of the query.
Hope the evidence needed to answer survived the ranking cutoff.
At small scale, the cracks can be easy to miss. At enterprise scale, they become consequential. A clause loses its exception. A table loses its heading. A follow-up question loses the conversation that gives it meaning. And the most relevant evidence may never appear—not because it is absent, but because it did not make the top of a similarity list.
In legal, financial, and technical material, passages can use nearly identical language while changing the result entirely. A different party. A different threshold. A different exception.
Similarity sees familiar words. It may miss the distinction that matters.
The best evidence is often phrased differently from the question, located in a referenced appendix, or dependent on earlier context in the conversation.
If it falls below the ranking line, it disappears without warning.
The issue is not that vectors are bad. It is that a library is more than a list of text fragments—and a serious question deserves more than a one-shot guess.
Coworker represents each document as a navigable tree, then extends that same idea across the whole knowledge estate.
Inside a document, the tree follows the natural structure a careful reader would use: sections, subsections, pages, tables, and content blocks. Across documents, a file-system layer connects every document tree into a larger, searchable whole.
The same reasoning policy can choose a branch in the enterprise tree, descend into a specific document, and follow its internal structure to the supporting source.
Instead of asking, “Which chunk is nearest?” Coworker asks a more useful question at every step: given this question, this conversation, and this place in the map, is this branch worth opening?
A conventional file hierarchy is a useful starting point. But at scale, it is rarely the map a person needs to answer a real question.
Large knowledge estates are often flat: a bucket of files, a SharePoint library, a document system with inconsistent folders. Coworker can synthesize semantic topic nodes and infer helpful attributes such as category, entities, summaries, or dates—creating branches with signals a reasoning system can actually use.
A contract may be about a vendor, region, product line, renewal date, and fiscal period at once. A single folder path forces one answer. Coworker can surface the same document through multiple meaningful routes, preserving the dimensions a query actually needs.
“What did vendor X charge us in 2024?” benefits from vendor → year. “Which contracts renew next quarter?” benefits from status → renewal date. Coworker assembles an informative view over the same corpus at query time—without requiring a new ingestion cycle for every way people work.
A million-document tree is only useful if the system knows when to inspect its branches—and when to skip the scenery.
Some labels carry genuine meaning for the question: active contracts, 2024, vendor X. In these places, Coworker moves layer by layer, using each label to prune the search intelligently. Other labels carry almost no signal: misc, final_v3, folder_1. In those places, a literal walk through the hierarchy wastes time and adds no understanding.
This dynamic flattening keeps search depth proportional to signal—not to the accidental complexity of the folders someone created years ago.
That is how a tree becomes practical at enterprise scale: not by forcing the model to read every branch, but by giving it permission to reason about which structure is worth reading.
AI answers are more dependable when the route to evidence is visible. Coworker is designed to make that route a first-class part of retrieval.
Represent documents and their relationships as a navigable knowledge structure.
Use the question, conversation, and emerging evidence to decide what to inspect next.
Make retrieval an understandable path, not a black box of ranked fragments.
It combines document structure, query-aware organization, and deliberate navigation so AI can move from a question to evidence with clarity—one useful branch at a time.
Explore Coworker →