Generate structured research prompts — from grant proposals to paper reviews — directly in the system. Define objectives, parameters, and scientific domains to activate agentic exploration.
Automatically create full-scale research reports across scientific disciplines. Each output includes structured evidence, citation tracing, RAG-enhanced internal document analysis, and synthesized conclusions.
Every insight comes with a clear trail: which agents processed it, what sources it came from, and how it connects to your broader research objective.
Customize templates for literature reviews, experiment plans, comparative studies, and meta-analyses. Define the logic structure of your outputs — or let agents auto-generate them based on your query.
Each query runs a full multi-agent pipeline, reducing days of manual literature review to just minutes — with no compromise on scientific rigor.
Compared to hiring full research teams or running subscription-based tools across databases, Astrolith automates structured discovery at a fraction of the cost.
Supports a wide range of scientific use cases — from hypothesis validation and literature surveys to experiment design and regulatory comparison.
Tweak search depth, topic granularity, citation density, source priority, and AI model selection — or deploy custom agents per research goal.
Enter complex scientific questions and receive structured responses built from both external literature and your internal documentation — all traceable to source.
Every insight comes with context — which agents retrieved it, what source it came from, and how it fits into the larger knowledge map. No more black box results.
Maintain consistent tone, terminology, methodology structure, and even analytical criteria across different queries, topics, and timeframes.
Configure the system to match your research domain, use your lab’s internal files, and prioritize results relevant to your field — biomedical, climate, materials, and more.
Deconstructs complex scientific questions into layered topics, enabling deep research using academic and domain-specific sources.
Pulls and processes information from arXiv, PubMed, Semantic Scholar, internal PDFs, and more — matching the needs of your research objective.
Highlights contradictions, cross-validates evidence across multiple sources, and flags unsupported claims before they reach the final report.
From exploratory reviews to comparative analysis and experiment design — customize agent behavior and output structure for any research lifecycle.
Compare outputs from different pipelines or agents. Use this to benchmark reasoning styles, data selection patterns, or conclusion divergence.
Use structured research templates — meta-analysis, literature review, systematic exploration — or create your own format for consistent scientific outputs.
Ask complex scientific questions in plain English. The system interprets intent, activates relevant agents, and returns structured insights — complete with citations and source context.
Generates concise summaries and highlights of long research outputs, documents, or papers — optimized for quick review without losing core findings or context.
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