What problem does it solve?
Researching effectively requires grounding on existing literature and turning project context into well-formed questions, but teams often struggle to organize related works and synthesize evidence into a coherent deep research report before experiments begin.
Core Features & Use Cases
- Project context framing: understands the active research project and extracts the problem framing needed for upstream research work.
- Research question development: creates or progresses research questions so later experiments have stable, tool-attached context.
- Progressive literature search and synthesis: searches papers, curates related works for durable memory, and produces a deep research report synthesized thematically with targeted citations.
- Use Case: Before planning experiments, investigate prior art and produce a deep research report that matches the project description’s language and organizes findings around themes rather than paper-by-paper notes.
Quick Start
Ask the AI to run pre-experiment research for your active project by checking in, retrieving or creating research questions, searching papers, curating related works, and saving a deep research report.