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Undermind35DraftDeep literature search and cited research reportsVerified 2026-10-13
Dossier · Research assistant
Undermind
A citation trail is a route into the literature, not proof that you found it all.
01
What is Undermind?
Decision
Deep literature search and cited research reports
Choose it when
Students exploring a specific literature question who can check the returned papers themselves
Researchers following citation trails beyond an initial reading list
Skip it when
You need an instant lookup rather than a search that may take several minutes
Your review protocol requires a verified database list and reproducible search coverage
Cost
Listed 13 Oct 2026: Free $0; Pro $16/month billed annually; Team $15/person/month billed annually; Enterprise custom.
Privacy
Checked 13 Oct 2026: the homepage's enterprise section says a company's proprietary R&D data stays isolated, is not used to train AI models, and is not retained or used for training by its model providers.
Watch for Deep search is not an instant lookup. The vendor reports an average production search time of about 2.9 minutes; we have not timed a search or checked delays on Free.
Undermind is a tool in the VerdictPal Research assistant set: Literature search that refines your question, follows citation trails and builds cited reports. Search coverage, citation reliability and individual-account data rules still need verification. It scores 35 out of 100 on editorial fit.
Role: Deep literature search and cited research reportsCategory: Research assistantSynthesized
Undermind at a glance, with the date each field was checked.
Field
Value
Editorial fit
35 out of 100
Dossier status
Draft
Set
Research assistant
Role in a workflow
Deep literature search and cited research reports
Pricing
Free Free · Pro (annual billing) $16/mo · Team (per person, annual billing) $15/mo · Enterprise Custom
Training on your data
Checked 13 Oct 2026: Undermind's homepage says a company's proprietary R&D data is not used for AI training and its model providers neither retain nor train on it, in the enterprise section. This does not establish Free, Pro or Team rules. The primary Privacy Policy and Terms were not read for this dossier; individual-account training and opt-out remain unverified. Do not assume an individual subscription inherits the enterprise promise.
Last verified
2026-10-13
Public facts · vendor & repo
List prices and pay-as-you-go entry points we can cite without running our own bench. Each tile links to a source when possible.
Non-price vendor claims — multipliers, caps, and API scope. Detailed matrices live in subscription and SDK sections below.
Search method
Follow-up questions, paper evaluation and citation trails are described on the homepage; exact databases remain unverified on 13 Oct 2026.Product homepage ↗
Search time
About 2.9 minutes on average for a production deep search, according to the vendor; no desk timing test.Vendor benchmark explanation ↗
Usage limits
Standard rate limits on Free, 10x higher usage advertised on Pro; exact allowances unverified as of 13 Oct 2026.Annual pricing section ↗
Enterprise training claim
No training or model-provider retention of a company's proprietary R&D data is advertised; individual-account policy not verified on 13 Oct 2026.Enterprise section, not primary Privacy Policy ↗
How it works · agent loop
The public positioning for this product — the loop we score against on VerdictPal.
01
Define the question and data boundary
Write a specific literature question and inclusion criteria. Start with public material; the enterprise marketing does not settle whether your individual account is suitable for confidential work.
02
Record the search, not just the report
For an authorised trial, retain the question, follow-up answers, date, returned papers and elapsed time. Check known relevant papers against the results. This is a proposed trial, not a completed search.
03
Open the cited papers
Check bibliographic details and read the passage supporting each report claim. Keep unsupported interpretations separate from missing or inaccessible sources. A relevance score is not evidence quality.
04
Compare coverage and actual limits
Use a library database search alongside Undermind. Compare the reading list with Elicit, Consensus or SciSpace using the same question; log rate limits before choosing an annual plan. No head-to-head result is published here.
02
Who it fits, where it fails
Best for
Students exploring a specific literature question who can check the returned papers themselves
Researchers following citation trails beyond an initial reading list
Readers who want to refine a cited report through follow-up questions
A controlled search with public material before deciding whether Pro fits the workload
Avoid if
You need an instant lookup rather than a search that may take several minutes
Your review protocol requires a verified database list and reproducible search coverage
You need confirmed Free or Pro training and retention terms before sharing unpublished work
You need a fixed search allowance or a verified monthly subscription price to budget the work
Strengths
Literature search from a natural-language research goal
Follow-up questions to refine the search
Deep search that follows citation trails
Research reports with inline source-paper citations
Report refinement through further questions
Full-text analysis advertised, with deepest analysis on Pro
Paper relevance scores, sorting and filters
Custom research tables
Topic monitoring and notifications about relevant papers
Shared workspaces
Agent connections advertised for Claude and ChatGPT
Weaknesses
Deep search is not an instant lookup. The vendor reports an average production search time of about 2.9 minutes; we have not timed a search or checked delays on Free.
As of 13 Oct 2026, the checked material does not establish the exact corpus or databases searched. A long result list cannot prove that all relevant or contrary studies were covered.
The retrieval benchmark is run by Undermind, using an LLM judge on abstracts and queries sampled from its own service. It is not an independent test of search completeness or whether report claims are supported.
A source link gives you a paper to inspect, not a checked conclusion. No citation trial was run for this dossier; open the paper and check the passage before using a report's claim.
Free lists standard rate limits and Pro advertises 10x higher usage, but no tested search allowance is available here. As of 13 Oct 2026, the exact limits and monthly Pro price remain unverified.
The no-training statement is framed around enterprise proprietary R&D data. As of 13 Oct 2026, it does not establish the rule for an individual Free or Pro account.
How it could improve
Citation accuracy needs investment. A citation-verification pass against Semantic Scholar or CrossRef before presenting a source would catch the worst fabrication errors.
Institutional policy friction could be reduced with a local/on-prem deployment option, or at minimum with clear data-handling documentation that compliance teams can review without an NDA.
Editorial fit scores 35/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Free-tier utility scores 40/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Cost-to-value scores 50/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Opportunity cost scores 0/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Source grounding scores 0/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Privacy posture scores 0/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Failure transparency scores 50/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Evidence strength scores 40/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Integration reach scores 40/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Reliability scores 0/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Data portability scores 0/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Transparency scores 50/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
Search modes · 4 lenses
One input box, many retrieval postures. Filter by tier.
01Free
Deep search
Iterative literature search with citation trails; check limits on the live product.
02Free
Citation trail
Follow references into the literature; this is not proof of full coverage.
03Pro
Pro usage
$16/month billed annually with advertised 10x usage; exact limits unknown.
04Pro
Connections & workspaces
Advertised Claude/ChatGPT connections and shared workspaces; unverified.
Competitive lens
Where Undermind wins for cited research — and where a rival still belongs in the stack.
Undermind's documented focus is deep search along citation trails and report refinement. Elicit's dossier focuses on evidence tables and review workflows. Compare the papers and fields you need; no head-to-head trial establishes a winner.
Undermind
Consider Undermind when following literature trails is the main task; this is a feature-fit question, not a tested advantage.
Elicit
Consider Elicit when structured extraction is the main artifact, with every field checked against its source.
03
Scores and evidence
Metric lab · 16 dimensions
Click a tile for the editorial note. Color follows score: coral, yellow, mint.
Shape
Avg 35 · 0–70
35Editorial fit
Provisional, source-synthesized; no hands-on testing. Weighted mean of 13 dimensions using the published rubric; flagged zeros for switching cost, source grounding, individual privacy and reliability remain included. Homepage/pricing/benchmark documentation is the basis, not a search trial.
By group
Fit58
Editorial fit35
Wedge task fit70
Feature depth70
Cost30
Free-tier utility40
Cost-to-value50
Opportunity cost0
Trust28
Source grounding0
Privacy posture0
Failure transparency50
Evidence strength40
Transparency50
Workflow31
Integration reach40
Setup friction60
Reliability0
Competitive position55
Data portability0
Editorial evidence · 3 entries
Task
Check annual-billing list prices
The 13 Oct homepage lists Free $0, Pro $16/month and Team $15/person/month billed annually, with custom Enterprise pricing.
↳ Vendor list prices, not a checkout or workload-cost test. Monthly pricing, sector differences and exact limits remain open.
Task
Read the vendor retrieval benchmark's method
Undermind describes 23 research goals sampled from its service, with relevance judged by an LLM from paper abstracts and a ten-minute comparison window.
↳ Vendor-run evaluation, not independently reproduced here. Retrieval relevance is not a measured citation-faithfulness result.
Task
Check the scope of the no-training statement
The homepage frames the statement around enterprise proprietary R&D data, including model-provider retention and training.
↳ Marketing wording, not a policy or contract audit. Free, Pro and Team rules remain unverified as of 13 Oct 2026.
Test scenarios · hands-on lab
How we exercised the product. Step through each run before you trust the scores.
Test run
Step 1 of 4
Choose a public literature question and record inclusion criteria and known relevant studies before searching.
Research log · desk notes
What we learned while building this dossier — not vendor copy.
The brief and notes record Pro at $16/month and Team at $15/person/month billed annually. The homepage recheck also shows 5 seats at $15 for Team. Monthly Pro, minimum seats and Academic/Industry differences remain open; no price is calculated from the annual saving.
The current evaluation uses 23 goals from Undermind's own query pool and an LLM judge on abstracts. It describes a ten-minute retrieval comparison, while the vendor says normal searches stop earlier on average. We do not repeat the older Google Scholar multiplier as a VerdictPal finding, or turn retrieval figures into citation-accuracy scores.
Only the homepage's enterprise wording was checked. The research note's secondary policy quotation is not treated as verified primary policy. No Free/Pro no-training, US-storage, SCC or data-sale clause is asserted without reading the applicable primary document.
04
What it costs, what it keeps
Pricing deck · checked 2026-10-13
Pro
$16 per month, billed annually
Monthly subscription price unverified as of 13 Oct 2026
Latest models and deepest full-text analysis advertised.
10x higher usage is vendor wording, not a measured search allowance.
Unlimited workspaces, files and paper libraries advertised; searches are not described as unlimited.
RAW · checked 2026-10-13: Listed 13 Oct 2026: Free $0; Pro $16/month billed annually; Team $15/person/month billed annually; Enterprise custom. The current page shows a Team configuration of 5 seats at $15, not a verified minimum-seat rule. Free advertises standard rate limits; Pro advertises 10x higher usage and unlimited workspaces, files and paper libraries. Exact chat/search allowances have not been verified or tested. The monthly Pro price and any Academic/Industry price differences remain unverified; no price is inferred from the advertised annual saving.
Privacy deep-dive · checked 2026-10-13
Checked 13 Oct 2026: the homepage's enterprise section says a company's proprietary R&D data stays isolated, is not used to train AI models, and is not retained or used for training by its model providers. It also advertises encryption in transit and at rest, customer ownership of inputs and outputs, and removal of an organisation's data on request. These are enterprise-framed vendor statements, not an audit or verified terms for Free, Pro or Team. The primary Privacy Policy and Terms were not read for this dossier. Individual-account training, retention and deletion rules, EU/CH residency and DPA availability remain unverified as of 13 Oct 2026. Do not upload confidential manuscripts, thesis material or patient data on the strength of the enterprise copy.
Training on your dataChecked 13 Oct 2026: Undermind's homepage says a company's proprietary R&D data is not used for AI training and its model providers neither retain nor train on it, in the enterprise section. This does not establish Free, Pro or Team rules. The primary Privacy Policy and Terms were not read for this dossier; individual-account training and opt-out remain unverified. Do not assume an individual subscription inherits the enterprise promise.
EU data residencyNo EU-only or Swiss data-residency option, processing region or DPA was verified as of 13 Oct 2026. Enterprise encryption claims do not establish a storage location; the research note's secondary policy quotation is not treated as verified primary policy.
SOC 2 attestationNot recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
Local-first by defaultNot recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
Under the hood
Vendors named on the product about page — useful for procurement and privacy reviews.
How this tool fits into a composed research stack:
Turn a research goal into a search questionFollow citation trails to candidate papersRefine a report and inspect its sourcesMonitor a topic for relevant new papers
Deep panes · 10 lenses
Editorial lenses only. Subscription and API pricing live in their own sections above.
What is Undermind?
Undermind is a web-based literature-search service. It asks follow-up questions, evaluates papers, follows citation trails and lets you refine reports with source links. Scores are provisional and come from documented sources, not hands-on testing or a desk-tested recommendation.
Students exploring a specific literature question who can check the returned papers themselves
Researchers following citation trails beyond an initial reading list
Readers who want to refine a cited report through follow-up questions
A controlled search with public material before deciding whether Pro fits the workload
Every answer below is assembled from the dated fields on this page. Nothing is written separately for search.
What is Undermind?
Undermind is a tool in the VerdictPal Research assistant set: Literature search that refines your question, follows citation trails and builds cited reports. Search coverage, citation reliability and individual-account data rules still need verification. It scores 35 out of 100 on editorial fit.
Is Undermind worth using?
Undermind scores 35 out of 100 on editorial fit and carries Draft dossier status. Its job in a research workflow is: Deep literature search and cited research reports.
Who is Undermind best for?
Undermind earns its place when you need:
Students exploring a specific literature question who can check the returned papers themselves
Researchers following citation trails beyond an initial reading list
Readers who want to refine a cited report through follow-up questions
A controlled search with public material before deciding whether Pro fits the workload
When should you not use Undermind?
Skip Undermind in these cases:
You need an instant lookup rather than a search that may take several minutes
Your review protocol requires a verified database list and reproducible search coverage
You need confirmed Free or Pro training and retention terms before sharing unpublished work
You need a fixed search allowance or a verified monthly subscription price to budget the work
What does Undermind cost?
Free Free · Pro (annual billing) $16/mo · Team (per person, annual billing) $15/mo · Enterprise Custom. Pricing last checked 2026-10-13.
Does Undermind train on your data?
Checked 13 Oct 2026: Undermind's homepage says a company's proprietary R&D data is not used for AI training and its model providers neither retain nor train on it, in the enterprise section. This does not establish Free, Pro or Team rules. The primary Privacy Policy and Terms were not read for this dossier; individual-account training and opt-out remain unverified. Do not assume an individual subscription inherits the enterprise promise.. Privacy terms last checked 2026-10-13.
What goes wrong with Undermind?
The dossier publishes 6 failure modes for Undermind, and they stay published whether or not the vendor likes them:
Deep search is not an instant lookup. The vendor reports an average production search time of about 2.9 minutes; we have not timed a search or checked delays on Free.
As of 13 Oct 2026, the checked material does not establish the exact corpus or databases searched. A long result list cannot prove that all relevant or contrary studies were covered.
The retrieval benchmark is run by Undermind, using an LLM judge on abstracts and queries sampled from its own service. It is not an independent test of search completeness or whether report claims are supported.
A source link gives you a paper to inspect, not a checked conclusion. No citation trial was run for this dossier; open the paper and check the passage before using a report's claim.
Free lists standard rate limits and Pro advertises 10x higher usage, but no tested search allowance is available here. As of 13 Oct 2026, the exact limits and monthly Pro price remain unverified.
The no-training statement is framed around enterprise proprietary R&D data. As of 13 Oct 2026, it does not establish the rule for an individual Free or Pro account.
What are the alternatives to Undermind?
The closest options to Undermind are Elicit, Consensus, SciSpace, Semantic Scholar. Each one that has a tool in the atlas is linked from this dossier, with a head-to-head comparison.
The Pack · Editorial newsletter
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