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 tiers$0every tool here has a permanently free plan

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

Perplexityfreemium, pricing not published
Elicitfreemium, pricing not published
Consensusfreemium, pricing not published
SciSpacefreemium, pricing not published
Semantic Scholarfreemium, pricing not published

Computed from each vendor's published monthly prices as we last verified them — tap a tool for its full pricing breakdown and price history.

All tools in this stack

Perplexity

freemium

Source-cited AI answer engine for live web research, file analysis, premium data...

Rating

4.6

Category

搜索研究与知识管理

Pricing

Perplexity has a free Standard tier for basic cited search. The main paid individual plans are Pro at $20/month and Max at $200/month. Education Pro is $10/month for verified students and educators. Enterprise pricing is per seat: Enterprise Pro is $40/seat/month or $400/seat/year, while Enterprise Max is $325/seat/month or $3,250/seat/year. Perplexity API usage is separate from web subscriptions and is billed by API request, token usage, tool invocation, sandbox session, and embeddings tokens.

Elicit

freemium

AI research assistant for searching papers, generating cited reports, and automa...

Rating

4.4

Category

搜索研究与知识管理

Pricing

Elicit has a free Basic plan, separate Academic and Industry price ladders, monthly and yearly billing, and a custom Enterprise plan. API access starts on Pro. Yearly billing materially reduces the displayed per-user monthly equivalent. The pricing page observed on 2026-07-08 also showed an Academic yearly Plus label that reads "Per user/year" while billing $132 annually; this entry records the displayed annual billing rather than normalizing it silently.

Consensus

freemium

AI research workspace for searching, synthesizing, and organizing evidence from ...

Rating

4.4

Category

搜索研究与知识管理

Pricing

Consensus has a free individual tier, paid Pro and Deep individual tiers, a Teams plan for organizations up to 200 seats, Enterprise custom pricing, and an application-only Search API. The official pricing page currently displays annual-equivalent individual pricing: Pro $12/mo billed $144 annually and Deep $45/mo billed $540 annually. The official team pricing page displays Teams at $20/seat/mo billed $240 annually and Enterprise as custom. The Help Center article last updated April 30, 2026 still lists Pro as $15/month or $120/year, and Deep as $65/month or $540/year. Because both are official sources, this record preserves the discrepancy rather than silently normalizing it.

SciSpace

freemium

AI research workspace for literature reviews, PDF reading, citation-backed writi...

Rating

4.2

Category

搜索研究与知识管理

Pricing

SciSpace has two official pricing surfaces. The current SciSpace Agent/Premium pricing uses monthly credits: Basic, Premium, Advanced, and Max. Credits are issued each subscription cycle, expire at the end of the cycle, and do not roll over. Credits power tasks executed inside SciSpace Agent; standalone tools outside the Agent experience do not consume credits. SciSpace also keeps separate editor/formatting plans under the Typeset pricing page; the official editor page says these editor plans are different from SciSpace Premium.

Semantic Scholar

freemium

Free AI-powered academic search engine for discovering, understanding, saving, c...

Rating

4.3

Category

搜索研究与知识管理

Pricing

Semantic Scholar is officially positioned as a free, AI-powered academic search and discovery tool. The Librarian Resources page says Semantic Scholar is free and open for all to use. Core search does not require login, while a free account unlocks library folders, saved papers, alerts, Research Feeds, and author-page management. The Academic Graph API and downloadable datasets are also presented as free and open resources, but API use is governed by rate limits, data licenses, attribution requirements, and acceptable-use restrictions. No official Pro, Team, Enterprise, seat, or usage-priced plan was found.

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.