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Jenni is an academic research and writing platform used by more than six million researchers, who have written over 15 million papers on it. The US-headquartered company says it passed US$10 million in annual recurring revenue (ARR) this year and is profitable, having grown almost entirely from revenue after raising only a small angel round.

While ChatGPT remains the first port of call for many researchers, Jenni has deliberately stayed narrow, focusing specifically on academic work. Its citations link to real papers rather than invented ones, users can trace every AI-generated claim back to the source documents behind it, and its AI Declaration feature, built for researchers submitting to publishers, lets them disclose exactly how AI was used in a paper.

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The platform can also work directly from a researcher’s own uploaded sources, which is crucial for local or non-English research, where general AI tools still default heavily to English-language results.

In Asia, Jenni has growing university partnerships and user bases across Singapore, Malaysia, India, Thailand and the Philippines, with China emerging as its most ambitious, and most complex, expansion target.

Justin Wong, Head of Commercial Operations, helped take Jenni from US$1 million to US$10 million ARR and now leads its B2B sales and international expansion. He spoke to e27 about the decisions that shaped that journey.

A bet on careers, not semesters

For Wong, there was no single dramatic inflection point in Jenni’s growth from US$1 million to US$10 million ARR. The change was a strategic repositioning, away from general writers and students and towards higher education and academic researchers.

The logic was straightforward. A student might use a writing tool for a single semester before moving on; a researcher writes papers across an entire career. Once Jenni reoriented itself around that longer relationship, building a product a researcher could rely on “from their first manuscript to their fiftieth”, retention, willingness to pay and word-of-mouth referrals inside academic labs and departments all improved.

That shift also reshaped operations. Once Jenni crossed the US$10 million threshold, the company went international“with intent”, localising for markets across Asia, Latin America, the Middle East and Europe. At the same time it built an institutional sales motion alongside its self-serve consumer business, largely because faculty were already bringing Jenni into their departments on their own.

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The bootstrapped path wasn’t originally a strategic choice; it became one. Jenni was one of the earlier players in AI-assisted academic writing, and revenue arrived early enough that raising a large venture round never felt necessary. That came with trade-offs. Every acquisition channel had to pay for itself, the team stayed lean with employees wearing multiple hats, and growth was never bought beyond what the business could sustain. The upside, Wong argues, was strategic independence. Without investor pressure to chase whichever segment was commercially hot, Jenni could commit fully to serving researchers over the long term.

Competing with the tab already open

Every researcher already has ChatGPT open somewhere, and Wong is candid that most conversations with universities start from that reality. The pitch isn’t that general models are useless. It’s that they invent citations, produce unsupported claims and generate prose with no identifiable source.

Jenni’s workflow is built to close those gaps:

• AskJenni lets researchers interrogate both their own uploaded PDFs and more than 250 million external papers.
• Autocomplete suggests one sentence at a time rather than generating full passages, keeping the researcher in control.
• Citations link to real sources across more than 10,000 citation styles.
• A four-layer review checks peer-review readiness, claim confidence, proofreading and tone before submission.

Jenni also does not train on user data in any feature.

Institutionally, Jenni avoids asking for a campus-wide commitment upfront. Instead, a department or lab runs a one-to-two-month pilot, tracking hours saved and manuscript activity, and the deployment expands based on the evidence. India-based Lovely Professional University’s pilot with more than 60 faculty members reported over 375 hours saved; Chulalongkorn University’s six-department rollout reached more than 300 users and over 750 hours saved.

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Asked what actually closes institutional deals, Wong points to Jenni’s Reviews feature. It scans a full paper for contradicted, unverified or misrepresented claims, whether a human or AI wrote them, and suggests peer-reviewed references to support them.

He, however, is careful not to reduce Jenni to a citation tool. Many users describe it, he says, as “a combination of ChatGPT, Microsoft Word, and Zotero in a single tab”: one workspace for organising sources, drafting and reviewing. That breadth, not any single feature, is what ultimately sells.

Publishers, not a publisher partnership

Wong is quick to correct a common misreading. Jenni didn’t build a feature with publishers. It built its AI Declaration feature for researchers submitting to publishers and journals, as a simple way to disclose exactly how AI was used in a paper, whether for citations, drafting or grammar correction. The declaration has now appeared in over 100 published papers, including work published by Nature, Elsevier, IEEE and MDPI. As journals formalise their AI disclosure policies, Wong sees this as part of making AI-assisted research broadly acceptable rather than suspect.

Localisation, and the China exception

Across Southeast Asia and India, Jenni’s self-serve-then-pilot motion travels well. China is the outlier. Traditional marketing and sales channels don’t work there. Researchers live on Xiaohongshu, WeChat and Douyin, behind entirely different infrastructure, which makes China the most heavily localised market Jenni has entered.

The payoff, Wong argues, justifies the effort: China publishes more research papers than any other country, and many of its universities lead global research output.

Language matters beyond China, too. Because Jenni grounds its AI in a researcher’s own uploaded sources, including local studies, theses and reports a general model wouldn’t surface, it can support hyper-local work in ways general chatbots cannot, since those often default to English-language searches. Jenni supports drafting in more than 30 languages.

This localisation sits alongside two sales cycles that feed each other. Individual researchers buy through free, monthly and annual plans; this is the self-serve business that passed US$10 million ARR in 2026. Institutions follow the slower pilot-then-expand model. Self-serve adoption is often how Jenni gets into an institution in the first place, because faculty and students are frequently already using it before a university formally engages. As universities increasingly centralise AI purchasing decisions, that dynamic turns individual subscriptions into campus-wide deployments.

The moat against bigger players

What stops a well-funded general AI company from simply adding academic features? Wong frames the defence around incentives rather than functionality. General tools optimise for fast, fluent answers sourced from anywhere on the internet, including unverifiable sources. Academic work demands citations a researcher can defend in front of reviewers. Jenni’s workflow is built around accountability, not just output speed: autocomplete, verifiable citations, four-layer review, no training on user data, and AI declarations. “Adding a citation button to a chatbot,” Wong says, “doesn’t replace that workflow.”

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With 15 million papers written and more than six million researchers on the platform, Wong believes Jenni’s next phase lies in depth rather than breadth: owning more of the research lifecycle rather than chasing new user segments. “If we’re the tool a researcher trusts for their whole career,” he says, “the new segments follow.”

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