Nace AI’s Drex for Search offers a high-accuracy, low-cost retrieval layer that enables AI coding agents to efficiently find evidence across massive document sets.
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Nace AI has released Drex for Search, which brings its Drex decision models to document search. Drex for Search is a retrieval layer that helps people and agents find evidence. Coding agents such as Claude Code, Codex, Cursor and OpenCode call it through CLI, SDKs, MCP or REST. In Nace’s evaluation, it scores 92.05% on BrowseComp-Plus with GPT-5, using 12.6 search calls per question. TL;DR Size: a hosted search system built to search over 1M+ files. It pairs with an answering model such as GPT-5, Gemini 3.5 Flash or Qwen 27B. Runs on: Nace’s managed cloud, a dedicated cloud, your own AWS or Azure account, or on-prem and air-gapped. Performance: ranked #1 or #2 on 3 of 4 public leaderboards, per Nace. Best: 77.5% R@1 on VisDocAgentBench, 10 points above Claude Opus 5 (67.5%). What did Nace AI release? Nace AI frames search as a series of decisions. Drex for Search applies its Drex models to those decisions. Drex scores given options with a calibrated probability each, in 1 forward pass. That makes it cheap enough for every agent step. The release targets classic RAG, which Nace sums up as chunk, embed, top-k, hope. The pipeline breaks in 3 cases: the answer sits in a table on page 23 the answer needs 2 hops across files the answer is inside a scanned PDF How does Drex for Search work inside an agent? Nace describes 3 steps: Classify: Before any search runs, Drex decides if the question needs a quick lookup or an agent. The agent route reads documents and queries tables. Squeeze: Drex walks the workspace level by level: folder, file, section, page. Inside a sheet, it narrows to table and column. Each level keeps only what can hold the answer. Answer: Only the kept pages and tables reach the LLM. The answer cites the file and page. Depth is fixed, so Nace says a million files take as many steps as a thousand. How much does it help coding agents? Nace AI tested 9 agent and model pairs on OfficeQA Pro, a 133-question benchmark over U.S. Treasury Bulle
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Nace AI’s Drex for Search offers a high-accuracy, low-cost retrieval layer that enables AI coding agents to efficiently find evidence across massive document sets.
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