Research Assistant
Summarize papers and extract key findings
Ingest academic papers, industry reports, or technical documentation and extract hypotheses, methodologies, key findings, and limitations in a structured format — enabling researchers to process 10x more literature in the same time.
Claude 3.5 Sonnet
for 2,000K tokens/month · 80% input / 20% output
WHY THIS MODEL
Claude 3.5 Sonnet produces the most accurate, sourced research summaries of any mid-tier model — it understands nuance, tracks contradictions across sources, and synthesizes findings rather than just regurgitating content. This makes it the best choice for knowledge workers who need reliable, citable answers.
ALTERNATIVE MODELS
IMPLEMENTATION TIPS
- 1
Use a standardized extraction schema for every paper: problem statement, methodology, key results (with numbers), limitations, and related work — this creates a machine-readable research database, not just readable summaries.
- 2
Chain extraction with a comparison step: after processing a batch of papers, ask the model to identify consensus findings, contradictions, and open questions across the set — this meta-synthesis is where the real research value lies.
- 3
Flag papers where the model's confidence in extraction is low (ambiguous methodology sections, preprints without clear results) and prioritize those for manual human review rather than trusting the extraction blindly.