Archeus in Science

Scientific research involves extensive literature review, data collection, analysis, and documentation. These activities consume the majority of researcher time, leaving less for the creative and intellectual work of science — forming hypotheses, designing experiments, and interpreting results. Archeus automates the time-intensive parts of this process.

Whether you are in academia, government labs, or industrial R&D, Archeus provides the tools to work smarter and faster. The platform understands scientific contexts — it can parse research papers, extract methodology details, interpret statistical results, and generate scientific documentation.

The scientific process benefits from automation at every stage: hypothesis formation through literature review, experimental design through data analysis, results interpretation through statistical testing, and publication through manuscript preparation. Archeus provides capabilities for each of these stages.

Archeus can scan academic databases like PubMed, arXiv, Google Scholar, and specialized repositories to find relevant papers. It extracts key findings, methodologies, sample sizes, statistical results, and citations, building a structured knowledge base from scattered sources across the internet.

The browser agent handles access restrictions within your authorized subscriptions. It follows citation chains to discover related work you might otherwise miss — forward citations that build on a paper, related work in adjacent fields, and methodological alternatives that offer different perspectives.

For systematic reviews and meta-analyses, Archeus automates the screening and extraction process. It applies inclusion and exclusion criteria consistently across thousands of papers, extracts standardized data points, and compiles evidence summaries that support rigorous synthesis.

Experimental data often comes in messy, unstructured formats from instruments, databases, and public repositories. Archeus can clean, transform, and normalize this data, handling common issues like missing values, outlier detection, format conversion, and unit reconciliation.

Custom skills let you define data processing pipelines specific to your field. Chain together parsing, transformation, validation, and visualization steps into automated workflows. These pipelines are documented, reproducible, and shareable with collaborators.

For large datasets, Archeus leverages parallel processing to handle data-intensive computations efficiently. Statistical analyses, machine learning model training, and simulation studies can run in parallel across multiple sub-agents, reducing computation time from hours to minutes.

Archeus maintains context across sessions, making it an ideal research companion. It remembers your projects, methodologies, preferences, and ongoing investigations. This persistent context means you can pick up where you left off without re-explaining your research goals and constraints.

Generated reports, summaries, and analysis documents are formatted for publication or sharing with collaborators. Archeus produces figures, tables, and statistical summaries that meet journal formatting requirements. The platform can also prepare data and code availability statements required by many journals.

For team research, Archeus project workspaces provide shared context. Team members can access common datasets, share processing pipelines, and collaborate on analyses. The workspace maintains version control and change documentation, supporting reproducibility requirements.