A workspace, not a chatbot
Discovery, analysis, collaboration, writing, and supervision in one shared space — with full provenance the whole way through.
The goals it moves
Not engagement metrics — the institutional outcomes that actually move the needle for a department or a provost.
Student persistence
Students who can navigate the literature don’t stall out. Wonders keeps momentum through the hardest stretch — the review.
Degree completions
Chapter 2 is where timelines slip. A guided workflow gets students to a finished, rigorous review — and across the line.
Capability building
Raise research capability institution-wide without hiring more methodologists — Wonders teaches the method as students work.
Efficient supervision
Supervisors see the process, not just the final PDF — feedback in the workspace, not endless email threads.
Integrity by design
Every claim traces to a real source, with proof of work — cutting AI-plagiarism risk by design, not after-the-fact detection.
Lower supervision cost
Less time per student on mechanical review work means real saved staff hours — and capacity reclaimed.
Built to fit your governance
It’s the first question a provost or a librarian asks — before “what can it do?”. Here’s the honest answer, on the three axes most AI policies share.
Human oversight by default
AI guides; people decide. Wonders doesn’t write papers — it teaches researchers to work critically with their sources. Nothing happens without a human in the loop.
Transparent provenance
Every citation, paraphrase, and any AI-assisted sentence traces back to its origin. Supervisors see exactly what a student cited, and how — no black box to take on faith.
Fair & reliable
Equitable access for every student, and we never train models on student or institutional work. Standards-aligned (W3C WADM, PROV-O) — auditable, interoperable, and durable.
More detail in the AI Safety Center →, and why detection is the wrong model →
Doctoral research, confidently
The literature review is where momentum dies and timelines slip. Wonders structures discovery, organization, writing, and supervisor collaboration into one workflow — so students finish the review with confidence and rigor, not at 3am the night before.
Critical research skills, built with structured guidance — and a reason for everyone to say yes.
Students — Research independence
Become independent researchers, with rigorous exploration guided step by step — not answers handed over.
Supervisors — Visibility & time back
See the process behind the work through shared workspaces. Transparent sourcing cuts plagiarism risk with built-in proof of work.
Research teams — More published
Less admin in the literature review — find the real gaps and get to publishable insight faster.
Leadership — Measurable outcomes
Scale research capacity without expanding support staff — and raise the institution’s research profile.
Case study · National University
National University put Wonders in front of its doctoral cohort to speed up thesis-topic discovery, raise citation quality, and ease supervision load. It stuck.
“It’s the only AI that teaches research while doing it.”
Andy Riggle — AVP, Office of Graduate Studies“A real research workflow — not just another AI tool. A true partner.”
(1) Survey sample of 147 users. (2) Pilot evaluations. (3) Anecdotal evidence from user surveys. (4) Repeated usage surveys across pilots and users.
For your library
Library-licensed resources surface through SSO/SAML-managed institutional access — and the tools your students and staff already rely on plug right in.
Security & deployment
Need our DPA, or to run a security review? Talk to us — a real person will work through it with you.
We’re independent and post-revenue — no outside investors steering us toward an exit, no roadmap hijacked by the next funding round. We answer to the researchers who use Wonders, and we’re building to last.
In practice that means strong uptime, pilot partners who renew, ratings and an 81 NPS we’re proud of — and when you have a question, you reach the people who build the product, not a ticket queue. Often, that’s the founder.
On three axes most policies share. Human oversight: AI guides, people decide — Wonders doesn’t write papers. Transparency: every citation, paraphrase, and AI-assisted sentence traces to its origin. Fairness & reliability: equitable access, no training on student work, and provenance built on open standards (W3C WADM, PROV-O) so it’s auditable and interoperable.
Yes. Library-licensed resources surface through SSO/SAML-managed institutional access, and the tools students and staff already use — EBSCO, OpenAthens, Zotero, Grammarly, LibKey — plug in. Full provisioning is part of an enterprise agreement.
Wonders is FERPA & GDPR-aligned, student data is private by default, and we never use institutional data to train AI models. We’ve also completed the HECVAT — the higher-ed community’s standard vendor security assessment. Need our DPA or to run your own security review? Talk to us — a real person will work through it with you.
It’s a research tool, not a writing tool. Every AI suggestion is traceable to its source, and shared workspaces give supervisors visibility into the research process — reducing plagiarism risk with built-in proof of work.
Browser-based with no installation. You get SAML/SSO, usage dashboards, live training webinars, and dedicated support. Most institutions start with a pilot for a department or cohort — typically 4–8 weeks with measurable outcomes — before scaling.
Unlike a chatbot, Wonders is a structured research workspace: search 550M+ academic sources, organize them visually, and write with every claim traceable to a real paper. The point isn’t a faster answer — it’s a better researcher.
Deployed at National University, under evaluation at KU Leuven and the University of Antwerp, with researchers at Cambridge, UC Berkeley, and Imperial College on the platform.
Start with a pilot for one department or cohort — typically 4–8 weeks, with outcomes you can measure. Talk to our team to scope it.
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