Surface prior art and relevant research — without leaving the platform.
Before any new formulation, any new product concept, any novel process can move forward, someone needs to answer: 'has this been done before? What does the literature say? Are there patents we should know about?' This literature review phase is critical — it prevents wasted effort on solved problems, identifies potential IP conflicts early, and surfaces approaches that might accelerate the work.
But literature search is time-consuming, skill-dependent, and often incomplete. A scientist might spend days searching databases, reading abstracts, and assembling a picture of the prior art landscape. The quality of that search depends entirely on the searcher's experience, their choice of keywords, and the databases they have access to. Important papers get missed. Relevant patents go unnoticed. The same search, done by a different person, might yield different results.
AI-assisted literature search doesn't replace the scientist's judgment — it amplifies their reach. By processing natural-language queries across broad source databases and returning ranked, relevant results with key-point extraction, it makes the 'what's already known' phase faster, more consistent, and more comprehensive.
Athena's Literature Assistant is an AI-powered research copilot embedded within the platform. Scientists describe what they're looking for in natural language — 'corrosion inhibitors for mild steel in acidic environments' or 'recent advances in mRNA lipid nanoparticle stability' — and the assistant searches across configured sources, returning relevant papers, patents, and prior internal project data with key findings highlighted.
Because the assistant lives inside Athena, it has context. A search initiated from within a project can factor in that project's domain, existing samples, and prior literature reviews. Results can be saved directly to the project's Literature tab, annotated, and shared with the team — no copy-paste from an external tool, no broken links, no lost context.
Each research session is preserved as a workspace — searchable, shareable, and referenceable later. When someone asks 'what literature did we review before starting Project X?', the answer is a click away, complete with the AI's analysis and the scientist's annotations.
Literature searches are saved within Projects. Findings can inform Sample design, Task planning, and stage-gate deliverables. Research workspaces are accessible from the project's Literature tab.
Every module is configured to your process — your terminology, your workflows, your approval chains. Login to explore how Athena works for your organization.
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