The Deep Research Stack

Mine papers, verify claims, and map literature with AI

The Deep Research Stack is a sequential AI-powered pipeline for systematic literature review and evidence synthesis. By combining five specialized tools in a deliberate order, you move from broad exploration to deep verification: start with Perplexity for a fast, cited overview of a topic; then use Elicit to discover relevant academic papers; Consensus to get synthesized answers from peer-reviewed literature; SciSpace to read and interrogate specific PDFs; and finally Semantic Scholar to map citations and context. This order minimizes wasted effort—each tool builds on the previous output, ensuring you never jump into deep reading without a map. The result is a comprehensive, verified literature map and a set of answer-backed insights. This workflow is for researchers, students, and professionals who need to thoroughly understand a new field or validate claims with scholarly evidence, without endless manual searching.

The workflow, step by step

1
Explore the topic with cited answers
Perplexity
Use Perplexity's deep research mode to get a broad, cited overview of your research question. It searches the web and surfaces key concepts, recent developments, and primary sources quickly. This step builds your initial mental map and provides leads for academic papers.
Hand-off → Export the list of key papers and claims mentioned in Perplexity's answers to use as search seeds.
2
Discover relevant academic papers
Elicit
Elicit excels at finding papers based on your research question, not just keyword matches. It uses AI to rank papers by relevance and extract key findings. This step turns your broad exploration into a targeted list of scholarly articles.
Hand-off → Export the list of relevant paper titles, abstracts, and DOI/links to feed into Consensus.
3
Synthesize consensus from papers
Consensus
Consensus takes your paper list and provides a summarized answer to your question based on the collective findings. It shows agreement/disagreement across studies, giving you a high-level evidence synthesis. This helps you quickly gauge the state of the field.
Hand-off → Note the key claims and consensus score, along with any conflicting results, to investigate deeper in SciSpace.
4
Read and interrogate specific papers
SciSpace
SciSpace allows you to upload PDFs and ask questions about the paper. Use this to dive into the most important or conflicting papers from your Consensus output. It explains methods, results, and jargon, saving hours of reading.
Hand-off → Extract detailed notes and specific citations from the papers you analyzed in SciSpace.
5
Map citations and context
Semantic Scholar
Semantic Scholar provides citation graphs, influential citations, and TLDR summaries. Use it to see how your key papers are cited—both supporting and contradicting works. This step gives you the final contextual map of the literature landscape.

What this stack costs per month

Running on free tiers4 of 5 steps freethe rest need a paid plan

All entry paid plans—no published monthly prices in this stack

Perplexityfreemium, pricing not published
Elicitpaid, pricing not published
Consensusfreemium, pricing not published
SciSpacefreemium, pricing not published
Semantic Scholarfree

Pricing

Free

Frequently asked questions

What does the full stack cost?

Perplexity, Consensus, SciSpace, and Semantic Scholar offer free tiers; Elicit is paid after a free trial. The full stack can be used for free with limited queries per day, but heavy use may require paid plans (typically $10-20/month per tool).

Can I skip any tools to save money?

Yes, if you only need a quick overview, you might stop after Consensus. However, skipping Elicit or Semantic Scholar weakens the depth of your literature map. The workflow is designed for maximum efficiency, but you can adapt based on your needs.

What's the best free alternative to this stack?

You can replace Elicit with Google Scholar and manual reading, but it's slower. For a free setup, use Perplexity (free tier) for exploration, then manually search Semantic Scholar and use SciSpace's free version for PDF analysis.

Should I start with Perplexity or go straight to academic search?

Always start with Perplexity. It gives you context and keywords before diving into academic databases, preventing you from searching in the dark. This saves time and improves search precision.

What common mistakes do people make with this workflow?

Skipping steps or doing them out of order. Another mistake is not exporting output between steps—manually re-entering data wastes time. Also, relying solely on AI summaries without verifying full papers can lead to misinterpretation.