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 Bulletins. Key highlights: 6% more answers right, 62% fewer steps, 56% fewer tokens, and 2.3x faster.
| Harness | Model | Accuracy as shipped | Accuracy with Drex for Search | Agent turns as shipped | Agent turns with Drex for Search |
|---|---|---|---|---|---|
| Gemini CLI | Gemini 3.1 Pro | 54.9% | 61.7% | 22.9 | 10.4 |
| Codex | GPT-6 sol | 66.2% | 70.7% | 14.3 | 8.6 |
| Hermes Agent | Opus 5.5 | 64.3% | 68.4% | 8.5 | 3.7 |
| Hermes Agent | Qwen3.8-27B | 56.4% | 60.2% | 20.1 | 6.5 |
| Claude Code | Opus 5.5 | 66.5% | 69.9% | 18.5 | 9.1 |
| pi | Opus 5.5 | 65.4% | 68.4% | 7.7 | 3.7 |
| OpenCode | Qwen3.8-27B | 51.1% | 53.4% | 23.7 | 10.0 |
| pi | Qwen3.8-27B | 43.6% | 45.9% | 24.3 | 11.1 |
| Codex | GPT-6 luna | 54.9% | 53.4% | 16.8 | 11.3 |
What does a workspace include?
- Files: PDFs and scans, xlsx, csv and Parquet, mp3 and mp4, docx, pptx, md, json and txt.
- Citations: every answer names the file and the page, row or minute it came from.
- Workspaces: isolated, with their own index and knowledge graph, and shareable with invited members.
- Storage: upload files or folders, or keep workspaces in your own S3 bucket or Azure Blob container.
- Zero retention: available with your own storage.
- Domains: Tax, M&A, SEC 10-K, Contracts, or your own.
How does it perform on BrowseComp-Plus?
BrowseComp-Plus asks a deep-research agent 830 multi-hop questions over about 100K fixed web documents. With GPT-5, Nace AI reports:
- 92.05% accuracy, #2 of 86 entries on the leaderboard.
- 12.61 search calls per question, 16.6x fewer than the only entry ahead (209.27 calls).
- 87.32% in a single pass with 8.93 search calls, the best of any entry under 10 calls.
- +20.4 points over the Qwen3-Embed-8B baseline, with the same GPT-5 model.