Data to Paper

Automates the complete scientific workflow from raw data to literature search, analysis, interpretation, and final paper writing.

Enables the AI to autonomously raise and test hypotheses or allows users to set specific research goals.

Systematically guides interacting LLM and rule-based agents through the entire conventional scientific research path.

Implements safeguards over standard statistical packages to minimize common coding errors made by large language models.

Generates verifiable research papers where any numeric value can be click-traced back to its specific originating code lines.

Creates manuscripts with a clear and traceable information flow, ensuring human verifiability of results.

Produces transparent outputs designed for human inspection and verification of the AI's research process.

Offers both fully autonomous research execution and human-guided interaction via a dedicated Copilot App.

Allows users to oversee, inspect, and actively guide the research, including providing reviews or invoking on-demand AI-reviews.

Provides functionality to rewind the research process to prior steps and record/replay runs for analysis and debugging.

Includes functionality to track and manage API usage and associated costs for language model interactions.