Set the working context
Choose the business, project goal, relevant knowledge, and the people responsible for reviewing the result.

Scope the decision, collect current evidence, combine relevant workspace knowledge, coordinate specialists, and return the result in the format the team needs.
No code · review gates · work organized by business
druidx run deep-research. loading business context. multi-step-investigation, break a broad research outcome into evidence collection, comparison, analysis, and synthesis. web-search-and-browsing, collect current public evidence when the question requires it. knowledge-and-rag, ground eligible projects in the workspace's controlled document set. specialist-agents, divide collection, analysis, review, and presentation across appropriate roles. structured-findings, keep observations and source context visible throughout the project. 5 steps complete · output saved to Library.
Keep the source trail and the finished deliverable connected to the research project.
Complex questions require more than a single search summary. DruidX supports multi-step research with project status, clarification questions, findings, approvals, and outputs that remain available after the run.
Break a broad research outcome into evidence collection, comparison, analysis, and synthesis.
Collect current public evidence when the question requires it.
Ground eligible projects in the workspace's controlled document set.
Divide collection, analysis, review, and presentation across appropriate roles.
Keep observations and source context visible throughout the project.
Return research as a report, document, presentation, spreadsheet, chart, or related artifact.
The objective, the execution, and the outputs stay connected, so nothing gets rebuilt in the next tool.
Choose the business, project goal, relevant knowledge, and the people responsible for reviewing the result.
Select the appropriate agents, models, tools, connectors, and boundaries for the work.
Inspect progress and outputs, answer questions or approvals, and keep the useful result in the workspace.
Useful outputs your team can open, share, and build on — not another stream of chat.
Apply the capability to a concrete business outcome with visible context and a defined deliverable.
Configure the approach once, then bring it into future projects without rebuilding every instruction.
Use the capability inside a larger workflow with explicit logic, timing, and human review where required.
Your next move
Set up a workspace, add the context that matters, and let your AI team turn the goal into reviewed, reusable work.