Closed science SaaS: where the money and the deployment are

This is one part of a series on research-agent systems. The landscape piece states where the field stands and sets all twenty-seven systems side by side: Agentic science harnesses: the landscape.

Deep dives: closed SaaS

Eight hosted, vendor-managed products. Pricing verified live on the vendors' pricing pages on 03 September 2026 and re-checked 21, 28 September, 5, 12, 19 and 29 October and 2 November 2026 unless marked otherwise.

Elicit

Elicit Research (public benefit corporation spun out of Ought; cofounder & CEO Andreas Stuhlmüller)

SaaS

Sentence-level citations

Team / origin / funding

Born at Ought (US nonprofit ML research lab). 25 Sep 2023: $9M seed led by Fifty Years (Basis Set, Illusian, Mythos, Julian, angels incl. Tom Preston-Werner, Arash Ferdowsi, Jeff Dean); became an independent public benefit corporation. 26 Feb 2025: $22M Series A at $100M valuation led by Spark Capital and Footwork. Product developed at Ought from ~2022; independent company announced Sep 2023.

Architecture

Self-contained hosted SaaS. Semantic search over 138M academic papers (Semantic Scholar, PubMed, OpenAlex) + 545K ClinicalTrials.gov trials. Components: Search, Research Agent (Aug 2026), Research Reports (up to 200 sources, customizable), Systematic Literature Review workflow (PRISMA 2020 auditable, screens up to 40,000 papers, dual review, extraction incl. tables/figures), Library, Alerts (daily OpenAlex updates), API + MCP on paid plans, browser extension for full text; the Library became a shared research hub for researchers and AI agents (17 Sep 2026); Routines (30 Sep 2026) make research recurring: the Research Agent monitors the literature on a daily or weekly schedule, updates the work and reports back for review (a Pro-plan feature). Find Papers, Extract Data and Chat with Papers folded into the Research Agent on 30 Sep, and legacy Alerts stopped running on 31 Oct 2026: anything not converted into a Routine no longer runs. Vendor evals: 95% search recall / 97% abstract / 99% full-text / 96% extraction across 994 Cochrane reviews (May 2026 blog); BioASQ claim (Jul 2026); SOC 2 Type II (Oct 2025).

Lit review capacity

Search: semantic + keyword over 138M papers + 545K trials; PRISMA-reproducible strategy; up to 1,000 papers / 20,000 data points at once; weekly updates, daily Alerts
Read/parse: full text when open access or via browser extension + subscription; else title/abstract; extracts quantitative/qualitative data incl. tables and figures
Synthesis: reports synthesizing up to 200 papers with methods section, PRISMA flow diagrams, paper-summary tables, search strategies; reusable data across projects
Citations: sentence-level: every AI-generated claim cites the exact sentence or figure; PRISMA-auditable exclusion reasons, per-criterion scores, supporting quotes

Pricing / hosting

Verified live at elicit.com/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Basic free (limited Research Agent/Reports; unlimited search/summaries/chat); Pro $49/user/month billed annually ($588/yr, 5,000-paper screening, API access); Scale $169/user/month billed annually ($2,028/yr, 5x usage, collaboration, figure extraction); Enterprise custom (40,000-paper screening, SSO/SAML, single-tenancy, unlimited API). Self-contained hosted SaaS; API on paid plans. X: @elicitorg. Docs: support.elicit.com (Intercom help center). GitHub: none (closed).

Positive

Identified studies missed by traditional searches; 41.8% precision vs 7.55% in original reviews; recommended as supplementary search toolhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12483133/
Feasibility study across environmental/life sciences: extractions met ≥87% accuracy thresholdhttps://www.cambridge.org/core/journals/research-synthesis-methods/article/...
Independent PMC evaluation: conclusions would have remained the same with Elicit's 6 found articles vs 17 from umbrella reviewhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11921719/

Negative / caveats

Sensitivity poor: average 39.5% (25.5-69.2%) vs 94.5% original, not sensitive enough to replace traditional searching at time of evaluationhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12483133/
r/PhD: "not reliable enough for any type of formal meta-research"https://www.reddit.com/r/PhD/comments/1istbkr/
Medium practitioner: Elicit's own rule of thumb ~90% of information shown is accuratehttps://medium.com/@borisnikolaev_57179/
IntuitionLabs audit (5 Sep 2026): no independent third-party accuracy audit of Elicit located; Elicit's own support notes concede its underlying models are "not inherently faithful" to source text, grounding claims cover existence, not claim supporthttps://intuitionlabs.ai/articles/citation-reliability-ai-literature-reviews

Neutral

Paperguide: Elicit = systematic-review/extraction engine vs Consensus = evidence discovery; neither covers full cycle alonehttps://paperguide.ai/blog/elicit-vs-consensus/
CJILS academic evaluation: Elicit best for synthesizing evidence; human oversight criticalhttps://ojs.lib.uwo.ca/index.php/cjils/article/view/23075

CoreSR

Researchers at University of Toronto, McMaster University, University Health Network, Toronto General Hospital (co-founders incl. Gordon Guyatt, McMaster HEI)

SaaS

Evidence synthesis

26 tools

TITAN-SR v6

Team / origin / funding

Built by researchers at University of Toronto, McMaster University, University Health Network and Toronto General Hospital. TITAN-SR paper team (published in the Journal of Clinical Epidemiology, Oct 2026; DOI 10.1016/j.jclinepi.2026.112514): Tyler Pitre (U Toronto), Dena Zeraatkar (McMaster), John Granton (U Toronto), Jason W. Busse and Bram Rochwerg (McMaster), Gordon H. Guyatt (McMaster HEI). Guyatt self-discloses as an equity-holding co-founder (X, 2 Sep 2026). Company entity not disclosed on site; commercial funding undisclosed (unverified). TITAN-SR study itself received no specific grant. Launch May-Sep 2026; exact date unverified.

Architecture

26 tools in one auditable project (the site's own count; the 12 Oct pass said 25): Auto-Review (agent-orchestrated pipeline with reviewer hard gates), Review Builder (PRISMA-P protocol, PROSPERO prep, referenced Word export), EviTrace Search (7 databases), Reference Screening (TITAN dual3 title/abstract triage + GLASS full-text review with verbatim evidence quotes), Data Collection, risk-of-bias instruments (RoB 2.0, ROBUST-RCT, ROBINS-I, ROBINS-E, PROBAST, QUIPS, CLARITY, JBI, QUADAS-2), INSPECT-SR trustworthiness, pairwise/dose-response/NMA/Bayesian NMA/CNMA/MBNMA meta-analysis (real R subprocesses), SMART GRADE (CoreGRADE/JUDGE), ICEMAN, Evidence to Decision, Manuscript Writer (GLASS locally hosted model), Living Reviews. Model line refreshed on 15 Oct: TITAN-SR v6 (an ensemble of three fine-tuned BERT-family encoders, trained on 3,097,955 records, calibrated on 80 held-out reviews and evaluated on 60 reviews it never saw; at the maximum-recall setting it kept 99.9% of final includes and screened out 78% of the rest, with a user-chosen sensitivity target of 93-99.5%), GLASS v3 (65,536-token context, six specialties) and ROB BERT v6 (22 specialist question heads across the five RoB 2 domains, 0.85 checker confidence gate). TITAN v6 runs on GPU tiers; the free tier keeps the older dual3 ensemble on CPU (up to 1,000 references a day); the site still does not claim that live models inherit the preprint benchmarks. Full audit trail, reviewer overrides with persisted reasons, role-based access.

Lit review capacity

Search: 7 databases in parallel: PubMed, Europe PMC, OpenAlex, ClinicalTrials.gov, Semantic Scholar, Scopus, Web of Science (Scopus/WoS via your own institutional API keys); PICO-based Boolean strategy generation; auto-dedup on DOI/PMID/title; seed recovery; citation-graph expansion; RIS/NBIB/EndNote/BibTeX/CSV/Excel/PubMed imports
Screening: TITAN-SR title/abstract triage + GLASS full-text review with verbatim evidence quotes; reviewer overrides; calibrated recall targets (e.g. 99%); PRISMA-ready counts
Synthesis: pairwise, dose-response, frequentist NMA (R netmeta), Bayesian NMA (JAGS), CNMA, dose-time MBNMA; unified imprecision engine; GRADE certainty with editable rationale; ICEMAN credibility; EtD (12 criteria); publication-quality SVG/PNG exports
Citations: PRISMA flow from live counts; GRADE Summary-of-Findings exports; referenced Word protocol export; manuscript writer grounded in project data; audit trail on every decision

Pricing / hosting

Verified live at coresr.ai/pricing 2026-09-03; re-checked 21, 28 Sep, 5 and 12 Oct 2026, a free-forever tier was added in the 28 Sep week (CAD); no changes in the 12 Oct pass. Free: "$0 forever... No card. Never expires. Zero AI", the manual systematic-review workbench (unlimited reviews, references and collaborators; import from any reference manager; dedupe with review and merge; two-reviewer title/abstract and full-text screening with conflict resolution; PRISMA 2020 flow and every export; manual extraction and Cochrane RoB 2.0 by hand; pairwise meta-analysis with GRADE certainty and Summary-of-Findings tables). Paid plans are now positioned as AI add-ons, each with a 7-day trial (card required): Essentials $39 CAD/mo (free workbench + AI on one active review; 1,000 refs/batch; 1,000 AI operations/day; monthly only); Researcher $99 CAD/mo or $999/yr (unlimited projects, 10,000 refs/batch, 7-db search, advanced analysis, ICEMAN); Team $349 CAD/mo or $3,839/yr (up to 6 members, Auto-Review, Manuscript Writer, Living Reviews, 25,000 AI calls/day); Institution custom (unlimited seats, EtD, SSO). Expert services (commissioned SRs, guideline packages) remain alongside. Hosted SaaS; TITAN models on CoreSR-managed tiers; GLASS locally hosted. X: @CoreSRR (4 followers, very new). Docs: no public docs site; in-app module library + YouTube channel + Pitre Substack tutorials. GitHub: none (API confirmed).

Positive

Co-founder Guyatt launch post: "We built CoreSR to strip that out and return the reviewer's time to judgment" (234 likes / 57 RT, 31 Aug 2026)https://x.com/GuyattGH
Peer-reviewed positioning: the site now leads with "Validated in the Journal of Clinical Epidemiology", citing 19,787 development reviews and 22 Cochrane test reviewshttps://coresr.ai
WSACS brought CoreSR in as the methods team for its clinical guideline update: 326 studies, 8 PICO domains, 135 GRADE profiles and 13 Evidence-to-Decision questions, with every study, judgment and number traceable in one workspacehttps://coresr.ai/services#wsacs
External academic software citation: Annals of Intensive Care 2026, a 26-trial network meta-analysis in critical care, cites CoreSR as softwarehttps://www.sciencedirect.com/science/article/pii/S211058202600155X
TITAN-SR method paper (figures from the SSRN preprint): outperformed four general-purpose LLM chatbots across 500 matched reviews (p <= 5x10^-31), S@99R 0.902 / AUC 0.974 on 3,434 internal test reviews; beat ASReview on 18/22 Cochrane temporal-holdout reviews (81.8%), median paired S@95R +0.145https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6732292
Determinism argument: BERT ensemble screening vs stochastic LLM screening, in Pitre's Substack walkthroughhttps://drtylerpitre.substack.com/p/ai-for-systematic-review-screening

Negative / caveats

Not yet on standard comparison lists: absent from Atlas and Paperguide 2026 systematic-review tool roundupshttps://www.atlasworkspace.ai/blog/ai-systematic-review-tools
Site claims TITAN-SR beat ASReview on 156/193 reviews (80.8%), still NOT in the SSRN abstract (18/22, 81.8% on 22 Cochrane reviews; abstract re-read via ResearchGate 21 Sep 2026). The site now also presents a per-model specificity table (TITAN SR 0.917 vs 0.746-0.660 for four LLM chatbots, n=500 matched reviews): direction confirmed by the abstract, per-model figures unverified. Do not quote without the preprint full text.https://coresr.ai

Neutral

No HN or Reddit presence found (searched); reception is early and founder-led
Site itself cautions the live dual3 service "has not inherited" the historical benchmark resultshttps://coresr.ai
The method paper is now presented on the site as peer-reviewed in the Journal of Clinical Epidemiology, where the 29 Oct pass still read it as in press at JCE; the site adds that the paper's historical pre-dual3 configuration figures do not validate the live dual3 ensemble; the DOI (10.1016/j.jclinepi.2026.112514) resolves to the Elsevier article page (verified 5 Oct 2026)https://coresr.ai

Consensus

Consensus AI (Consensus NLP); founders Eric Olson (CEO) and Christian Salem (CPO)

SaaS

250M+ papers

Team / origin / funding

Founded 2021 by Eric Olson and Christian Salem, former Division 1 athlete teammates; San Francisco-based. Seed: $3M announced Jan 2022 (VentureBeat; total $4.25M incl. Winklevoss Capital pre-seed reinvestment) with an OpenAI partnership. Series A: $11.5M led by Union Square Ventures. Series B: $30M led by GreatPoint Ventures. About page states $45M total funding from USV, GreatPoint, Nat Friedman, Daniel Gross.

Architecture

Self-contained hosted SaaS. Draws on 250M+ research papers including licensed full-text content from leading publishers. Components: AI search (natural-language + filters), Deep Search (builds a comprehensive search strategy, expands key terms, identifies conflicting arguments, explores the citation graph), Consensus Meter (instant visual agree/disagree on yes/no questions), Medical mode (~50,000 clinical guidelines + 8M articles from top 1,000 medical journals), natural-language filters, report drafting, compare-two-approaches, find-studies-by-method, citation graph visualization, API & MCP on paid plans. On 27 Oct 2026 the Research Agent became callable through the API and MCP as Threads (POST /v1/threads, or create_thread over MCP): a research question returns a cited synthesis with supporting quotes and the source sections they came from, and a follow-up can narrow the question or examine conflicting results. Two named integrations: Owkin wired the Consensus MCP into K Pro, its AI scientist, and reports 25x the paper coverage, and Phylo screens about 20,000 drug targets against hundreds of diseases through the API. 170+ university library partners; 10M+ users.

Lit review capacity

Search: over 250M+ papers incl. licensed full text; Deep Search expands key terms and explores the citation graph; natural-language filtering; medical mode; corpus source not publicly disclosed (per Paperguide)
Read/parse: analyzes abstracts and licensed full text; study snapshots show year, type, citation count
Synthesis: Deep reviews (comprehensive multi-study literature reviews, 3/mo free, 15 Pro, 200 Deep); narrative answers with numbered citations; Consensus Meter for directional evidence; conflicting-argument identification; report drafting
Citations: answers link to sources with numbered citations and paper takeaways

Pricing / hosting

Verified live at consensus.app/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Free $0 (10 Pro messages/mo, up to 3 Deep reviews/mo); Pro $12/mo annual ($144/yr) or $20 monthly (500 API & MCP uses/mo included); Deep $45/mo annual ($540/yr) or $65 monthly (2,000 uses/mo); Student/Faculty/Clinician up to 40% off; Teams/Enterprise custom. Self-contained hosted SaaS. X: @ConsensusNLP. Docs: help.consensus.app. GitHub: none found.

Positive

r/GradSchool: "Consensus is an excellent new AI research tool" (favourite new AI tool thread)https://www.reddit.com/r/GradSchool/comments/125ho7u/
Peer-reviewed Cureus review covers the Consensus App as a tool to retrieve and synthesize peer-reviewed literaturehttps://www.cureus.com/articles/370288/
Academic Help review: proven safe and effective for locating academic sourceshttps://academichelp.net/reference-finders/consensus-review.html

Negative / caveats

CJILS: Consensus often fails to incorporate theoretical depth: surfaces factual findings rather than broader theoretical implications; may not include most up-to-date research; no option to upload one's own articleshttps://ojs.lib.uwo.ca/index.php/cjils/article/view/23075
Paperguide: corpus size not publicly disclosed; no systematic-review screening or structured data extractionhttps://paperguide.ai/blog/elicit-vs-consensus/
IntuitionLabs audit (5 Sep 2026): no independent third-party accuracy audit of Consensus located; its "all citations are real, never hallucinated" claim (help center) addresses whether sources exist, not whether they support the claims madehttps://intuitionlabs.ai/articles/citation-reliability-ai-literature-reviews

Neutral

PapersFlow: Consensus is an answer engine ("What does science say about Y?") vs Elicit's discovery/extraction tool: different jobs, not direct substituteshttps://papersflow.ai/blog/elicit-vs-consensus-ai
Paperguide final verdict: Consensus stronger for fast quality-filtered evidence answers at lower entry pricehttps://paperguide.ai/blog/elicit-vs-consensus/

SciSpace

SciSpace (formerly Typeset); founders Saikiran Chandha (CEO) and Shanu Kumar (co-founder)

SaaS

280M+ papers

Team / origin / funding

Founded 2016 as Typeset (per Inc42 citing company website). Co-founders Chandha (VIT alumnus) and Kumar (IIT Kharagpur alumnus) met at a hackathon, developed the idea at Stanford Ignite. Rebranded to SciSpace with AI Copilot ~2023. Raised an undisclosed amount from Inventus Capital and Silicon Valley Quad (Inc42). Third-party revenue estimate $5.1M (2024, upmarket.co: unaudited).

Architecture

Self-contained hosted SaaS. Corpus 280M+ papers across multiple databases (Google Scholar, PubMed, arXiv, OpenAlex, Semantic Scholar: per scispace compare page). Components: Literature Review search with Deep Review mode, Chat with PDF (Copilot), AI Writer, Find Topics, Paraphraser, Citation Generator, Extract Data, AI Detector, Agent Gallery (SLR agents: Literature Review Generator, SLR Data Extraction, Risk of Bias RoB2/ROBINS-I, Inclusion/Exclusion Criteria, MeSH Term Extractor, Abstract Generator), connectors (Zotero, Mendeley, PubMed, Google Scholar, arXiv, Grants.gov), exports (DOC, PPT, LaTeX, PDF). Citation Verifier (7 Oct 2026), which checks a bibliography against indexed sources. Agent workflow: Run Task → Find papers → Group themes → Draft review → Export DOC, with audit logs; unlimited agent usage arrived on the Advanced and Max plans (9 Oct 2026). Inc42: uses OpenAI + Anthropic as foundational models with proprietary fine-tuned models on top. Vendor benchmark (flag as vendor-reported): Deep Review returned 26.3 highly relevant papers/query vs Elicit 13.0 and Consensus Pro 16.1 across 200 queries.

Lit review capacity

Search: 280M+ papers via multi-database retrieval; Deep Review mode; vendor claims up to ~700 papers read / ~312 selected in Deep Review (per Paperguide)
Read/parse: Chat with PDF for single- and multi-paper Q&A; extract structured data from PDFs into tables; reads abstracts to verify claims
Synthesis: Deep Review reads and synthesizes across many papers; literature review generator agent produces thematic grouping + draft review; AI Writer drafts with reference management
Citations: AI answers with citations; line-by-line inline citations; citation generator (APA/MLA/Chicago/IEEE/Vancouver); export CSV/XLSX/BIBTEX

Pricing / hosting

Verified live at scispace.com/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (same prices). Free signup; Premium $20/mo monthly or $12/mo billed annually (1,200 monthly credits); Advanced $90/mo monthly or $70/mo billed annually (now advertised with unlimited monthly usage, verified report, 8 parallel agent chats); Max $200/mo monthly or $160/mo billed annually (unlimited monthly usage, 16 parallel chats, priority support); Enterprise custom. A third SCI30 flash sale (extra 30% off yearly plans) was live to 12 Oct 2026, after windows that closed 21 Sep and 5 Oct. Self-contained hosted SaaS. X: @scispace. Docs: scispace.com/help. GitHub: none found for main platform.

Positive

r/research: "It's like having a decent research assistant on the side, not a magic solution, but it does save time"https://www.reddit.com/r/research/comments/1je29w4/
CJILS: SciSpace made headway as the best option for writing and teamwork; 4.3 stars Trustpilot (346 reviews)https://ojs.lib.uwo.ca/index.php/cjils/article/view/23075
Vendor benchmark (vendor-reported): 26.3 highly relevant papers/query vs Elicit 13.0, Consensus Pro 16.1https://scispace.com/resources/scispace-vs-elicit-vs-consensus-an-ai-literature-search-benchmark-across-200-queries/

Negative / caveats

r/academia: "Avoid SciSpace - Extremely Misleading, Overpriced, and Potentially a Scam. The quality is garbage. The pricing model is predatory."https://www.reddit.com/r/academia/comments/1p85o4a/
r/PhD: "output quite unreliable and the paraphrasing tool is verbose to the point of unusable"; "works really bad (photonics/laser physics)"https://www.reddit.com/r/PhD/comments/1gj0g3b/
aidetectplus review: 3 pros, 5 cons: incl. inability to locate uploaded PDFs againhttps://aidetectplus.com/blog/scispace-review

Neutral

Paperguide: SciSpace broader (discovery-to-drafting with AI writer); Elicit deeper for SR screening; SciSpace does not do pooled statistics, risk-of-bias assessment, or meta-analysishttps://paperguide.ai/blog/elicit-vs-scispace/
CJILS: none of the three tools provides a comprehensive solution; human oversight and judgment remain essential

Undermind

Undermind (YC Summer 2024); founders Joshua Ramette (CEO) and Tom Hartke (CTO): MIT quantum physics PhDs

SaaS

200M+ papers

MCP server

Team / origin / funding

Founded 2023, YC Summer 2024 batch, San Francisco. Whitepaper "Benchmarking the Undermind Search Assistant" (Hartke & Ramette, Jan 5, 2024) describes the v1 arXiv-based search engine. Launch HN 26 Jul 2024. Company describes itself as "your AI co-researcher for the literature"; "finds the papers others miss". Enterprise: GSK case study: "1000+ GSK scientists use Undermind"; Kim Branson (SVP/Global Head AI-ML, GSK) quote.

Architecture

Deep semantic search: LLMs as reasoning engine + classifier at key steps of a structured search process; four steps: (1) basic search combining semantic embeddings, citations, LLM reasoning; (2) relevance classification (highly relevant / closely related / ignorable); (3) adaptation & exploration (citation trails, mimicking human discovery); (4) comprehensiveness estimation via exponential "discovery curves". Now searches 200M+ papers; reads full texts, figures, tables, equations; MCP server exposes tools (launch_deep_search, search_papers, find_papers_by_author, read_pdfs, get_paper_info, etc.). MCP connectors published for ChatGPT, Claude, Cursor, VS Code, Codex, Claude Code, Copilot, Opencode, Grok, Perplexity.

Lit review capacity

Search: deep semantic search beyond keywords (10x vs Google Scholar in vendor whitepaper, ~300 user queries: vendor-reported); follows citation trails and key authors until "new searches stop finding relevant papers"; alerts on new relevant papers
Read/parse: evaluates hundreds of papers; full-text deep analysis on Pro tier; reads PDFs incl. figures, tables, equations; auto-fetches open-access PDFs
Synthesis: reports with citation panels; brainstorm with AI expert that has read the literature; custom tables; novelty/gap/cross-disciplinary use cases
Citations: in-line citations traceable to source paper; "verify any statement by following in-line citations back to the source paper"

Pricing / hosting

Verified undermind.ai pricing 2026-09-03; re-checked 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Free $0 (exploratory research, standard rate limits); Pro $16/mo billed annually (latest models, deepest full-text analysis, 10x usage); Team $15/person/mo annual, 5 seats; Enterprise custom (increased compute, SSO, admin dashboard). Hosted SaaS with MCP connectors; enterprise "Secure Cloud" (encryption, no training on customer data, own IP). X: @UndermindAI. Docs: site itself (pricing, FAQ, whitepaper, MCP page). GitHub: no public product repo (MCP server exists).

Positive

GSK case study: "Undermind has the greatest impact compared across all GenAI solutions in our scientific space: GSK R&D Leader"; 1000+ researchershttps://www.undermind.ai/
r/academia: "I recommend undermind.ai, it searches through all of semantic scholar and has a free tier"https://www.reddit.com/r/academia/comments/1kxrwlx/
r/PhD: "the current leaders are Undermind.ai and Elicit.com"https://www.reddit.com/r/PhD/comments/1jpvkbb/

Negative / caveats

Vendor-reported benchmarks: 10x vs Google Scholar figure is self-published in the whitepaper (Jan 2024, arXiv-only era)https://www.undermind.ai/whitepaper.pdf
Vendor-run benchmark (seen at the 28 Sep 2026 check): the site claims its v2 engine "outperforms frontier agents with web search by a wide margin": e.g. 85% vs 50% for GPT-5.6 Sol at 10 minutes across 23 research goals, LLM-graded; self-reported, no independent replicationhttps://www.undermind.ai/
Tooliverse review: "one of the best academic search tools available, but it works best when the scope of your research question is focused rather than broad"https://tooliverse.ai/tools/undermind

Neutral

YC Launch HN thread (July 2024) with founder engagementhttps://news.ycombinator.com/item?id=41069909
Whitepaper transparent about methodology (~300 queries, 2023 data)

Manus

Butterfly Effect Pte. Ltd. (Monica.im); founder Xiao Hong, chief scientist Ji Yichao ("Peak")

SaaS

General-purpose agent

Team / origin / status

China-founded (offices originally Beijing/Wuhan), HQ relocated to Singapore mid-2025; developed from Oct 2024; designed for markets outside China. Invitation-only beta 6 Mar 2025; Manus 1.6 (15 Dec 2025); desktop app 1.7.2 in Sep 2026; Manus 2.0 shipped 28 Sep 2026 (Cascade harness, Manus Studio desktop, Cloud Computer, event-triggered Automations, and Cue: a separate personal-agent app in free early access). Status: Dec 2025 Meta agreed to acquire (~US$2-3B); China's NDRC blocked the deal 27 Apr 2026; Meta cut ties 15 Jun 2026; Manus resumed independent operations (announced 1 Sep 2026 blog). Post-independence: a US$500M+ raise closed 8 Oct 2026: the first round since the Meta unwind, co-led by Boyu Capital and IDG Capital with Tencent, HSG and ZhenFund participating; ~US$4B valuation reported by Bloomberg; no Hong Kong IPO before 2027.

Architecture

General-purpose autonomous "hands-on" agent. Runs in a complete sandbox (virtual computer with internet, persistent file system, installable software/custom tools). Plan → execute multi-agent architecture with specialized sub-agents; iterative loop (analyze → plan → execute → observe); long-running sessions with context memory; multi-model dynamic invocation reported (e.g. Claude 3 for reasoning, GPT-4 for coding, Gemini for knowledge). Produces end deliverables (slides, websites, apps, reports). Manus 2.0's Cascade harness (28 Sep 2026) keeps the active context light and loads specialist capabilities only when needed: company-reported from one tested configuration: 23.2% fewer tokens, 28.2% less time and 32% lower cost than the previous system.

Lit review capacity

Search: live web browsing from its own cloud browser; Wide Research feature for broad search; not tied to academic databases
Read/parse: reads web pages, files, datasets, PDFs as part of task execution
Synthesis: multi-agent synthesis producing comprehensive reports plus artifacts (slide decks, websites, spreadsheets); positioned as "action engine"
Citations: not citation-rigorous: source links and a replayable trail of every page visited (provenance trail), but no formal citation system; flagged as weaker than OpenAI Deep Research for citation accuracy in comparisons

Pricing / hosting

Freemium SaaS. Official pricing page lists credit tiers 4,000 / 8,000 / 40,000 credits per month plus 300 refresh credits daily (numbers JS-rendered on official page); third-party pricing guides (nocode.mba, felloai) report Standard $20/mo (4,000 credits), Customizable $40/mo (8,000), Extended $200/mo (40,000), Team from ~$20/seat/mo, annual ~17% off. Prices marked third-party-reported. Hosted SaaS at manus.im (cloud sandbox); desktop (macOS/Windows); API at open.manus.ai/docs. Manus 2.0 adds purchasable Cloud Computer environments (persistent machines for automations and multiplayer projects). Manus Flex (29 Sep 2026) lets users bring their own inference-provider API key (initial partners OpenRouter, Fireworks, Modal); inference is billed by the provider. No open-source repo. X: @ManusAI_HQ. Docs: manus.im/docs.

Positive

HN: "Manus has been the best agent for turning text into work -- useable slides, code, extracting data from websites... it's the best"https://news.ycombinator.com/item?id=46428844
Launch demo drew 1M+ views in 20 hours; revenue run rate ~$90M (Aug 2025) → ~$125M (Dec 2025) per reporting; Meta agreed to acquire for ~$2-3B (later blocked)https://en.wikipedia.org/wiki/Manus_(AI_agent)

Negative / caveats

r/ycombinator "What happened with Manus?": hype faded after launchhttps://www.reddit.com/r/ycombinator/comments/1l2yi6x/
Firecrawl: "broader than it is deep. For pure research quality, ChatGPT Deep Research and Perplexity Deep Research produce better reports. The credit economy is opaque."https://www.firecrawl.dev/blog/best-ai-for-research
r/AIAssisted deep-research comparison: Manus "painfully slow... the outputs ended up looking very templated"https://www.reddit.com/r/AIAssisted/comments/1n84wy8/
UX Collective early-access review: initial hype 7-9/10 dropped to 3-5/10 after hands-on usehttps://uxdesign.cc/manus-ai-real-or-hype-404254922290

Neutral

NewsCatcher 2026: positions Manus as "action engine" with replayable page-visit trail for provenancehttps://www.newscatcherapi.com/blog-posts/best-deep-research-tools
Meta acquisition saga (announced Dec 2025, blocked Apr 2026, unwound Jun 2026, independent again Sep 2026)
Manus 2.0 launch coverage (28 Sep 2026): mainstream tech-press pickup; Cascade efficiency claims are company-reported from one tested configuration, with no independent benchmarks yet
Manus Flex (29 Sep 2026): bring-your-own-key module: the harness, tools and execution environments usable with a supported provider account; initial inference partners OpenRouter, Fireworks and Modal

ClawsGO Science · added 9 Nov 2026

BitMiracle AI Lab: a cloud-resident research agent, released and open-sourced 1 Nov 2026

SaaS

Cloud lab

New · 1 Nov 2026

What it is

A long-horizon research agent built as an always-on cloud workstation, not a chat tool. Each task is handed its own remote machine, and the agent can work for days: it runs clusters of agents in parallel, reaches 533 tools across 61 disciplines and 91 research databases, and returns a finished paper in LaTeX with every figure rebuilt as a reproducible bundle. BitMiracle AI Lab released it on 1 Nov 2026 and open-sourced the code for local verification.

Launch case study

The launch demonstration is the Global Attractor Conjecture, a 50-year-old problem in chemical reaction kinetics. ClawsGO Science returned a proof in 11 days as 83 Lean 4 files, about 9,300 lines, machine-checked with no sorry placeholders. The company describes a split architecture: Claude Opus 5.5 as the planning brain and 136 GPT 6 Astra agents writing the proof, with humans setting the goal and signing off the result. No independent evaluation of that run has been published.

Pricing / hosting / provider tie

Hosted cloud lab; the source is released for self-verification. Plans at the 9 Nov check: Free, Plus $9.90/mo, Pro $29.90/mo, Max $99.90/mo and Ultra $299.90/mo, in US dollars. Provider tie: vendor-managed mix (Claude Opus 5.5 plus GPT 6 Astra at launch); no BYO-model detail published. X: none verified. Repo: source released 1 Nov 2026; repo stats not verified in this pass.

Positive

Strong launch reception: Product Hunt #2 of the day (279 upvotes), unusual for a science harnesshttps://chatgate.ai/post/clawsgo-science
Scope is broad and concrete: 533 tools, 61 disciplines and 91 databases in the published capability list, with a machine-checkable proof as the launch artefacthttps://chatgate.ai/post/clawsgo-science

Negative / caveats

New and vendor-reported: one week old, no independent evaluation of the proof run or the capability counts locatedhttps://chatgate.ai/post/clawsgo-science
The Lean proof is a result about a proof artefact, not about the harness: Lean 4 checks the proof itself, but nothing yet shows the harness generalises beyond a demonstration curated for launch

Neutral

Pricing is credit-based at the upper tiers; the free tier is enough to test the workflow, not for long-horizon runs
Positioning overlaps the cloud-lab direction of Claude Science, but ClawsGO is a standalone vendor rather than a vendor-tied layer

Haiqu AgenticOS · added 12 Oct 2026

Haiqu (quantum middleware): a team-of-agents system for quantum research, unveiled 7 Oct 2026

SaaS

Quantum R&D

New · 7 Oct 2026

What it is

AgenticOS coordinates teams of specialised AI agents across a quantum research project: literature review, first-principles derivations, quantum-feasibility assessment, algorithm and circuit design, with results checked against classical baselines, critic agents, formal verification and human sign-off gates before a finished plan is handed to Haiqu's quantum software stack for hardware execution. Builds on Haiqu's Agentic Quantum OS (May 2026).

Launch evidence (company-reported)

Unveiled 7 Oct 2026. Flagship demo: starting from a scientist's idea, AgenticOS planned and ran a 3x3 Hubbard-model experiment on an IBM quantum computer, reporting a 75% circuit-depth reduction and a calibration that aligned hardware results with an error-free simulation of the same method. The company frames the design goal as catching science-altering agent mistakes; no independent evaluation is published yet.

Pricing / access

Closed platform; included with the Haiqu OS, available to enterprise R&D teams on request. Academic researchers can apply for free access through Haiqu's Academic Program (including SDK credits). No public repo, no BYO-model detail published. X: no dedicated handle verified. Site: haiqu.ai. Coverage: The Quantum Insider, Quantum Zeitgeist, AZoQuantum.

Neutral

One week old; trade-press coverage only: capability claims are company-reportedhttps://thequantuminsider.com/2026/10/07/haiqu-releases-agenticos-to-plan-and-check-quantum-research/
Not a literature-review product: the design emphasis sits in verification-first experiment planning; no benchmark comparisons publishedhttps://quantumzeitgeist.com/agents-complete-quantum-projects-haiqu/