About

Built for the people who turn research into commercialization decisions.

The intelligence layer for technology transfer offices, corporate R&D scouts, and investors evaluating deep tech.

Why we built it

The world is producing more research than any human team can read. PubMed adds thousands of biomedical papers a week. arXiv adds thousands more. The patent corpus grows by hundreds of thousands of filings a year. Public funding programs spawn and close on their own timetable. And the people whose job is to make commercialization decisions on top of all that — TTOs, corporate scouts, deep-tech investors — are still doing it with a search bar and a spreadsheet.

The synthesis tools that exist today are siloed. Patent intelligence platforms know patents but not the academic landscape. Academic search engines know papers but not funding flows. Generic AI chatbots can write summaries but can't cite their sources, can't validate IPC codes, and can't be defended in front of an IP committee. We built AI ResearchPartner to be the missing layer.

Research questions in. Commercialization-ready intelligence out — in minutes, not weeks.

Methodology

Every analysis is grounded in real source data. We query eight established source families in parallel — including the full US patent corpus — fuse the results, validate IPC codes against the actual patent classification system, score white-space opportunities on six independent axes, and surface a prioritized shortlist of next moves — every claim cited inline, every citation click-through to the source.

Beyond synthesis, the analysis protocol runs a set of novelty checks: publication velocity against patent-filing velocity, cross-domain method bridges, citation negative space, at least one flagged contrarian pick per analysis, and source-set diversity scoring on every claim. The white-space identification runs as a separate reasoning pass after the structured extraction, so you can tell what's analyst output versus AI augmentation. We surface confidence indicators when something is interpretation rather than tool-grounded fact. Your domain experts validate.

Data sources

PubMed (NIH/NLM, 36M+ biomedical citations) · arXiv (2.4M+ preprints across physics, CS, math, biology) · OpenAlex (240M+ scholarly works with author and institution metadata) · USPTO (full US patent corpus — assignees, inventors, IPC classifications, citation graph) · NIH RePORTER (live grant data) · Grants.gov (all US federal grant programs) · ClinicalTrials.gov (active and completed trials) · authoritative web search (Tavily) for industry reports and analyst data.

Eight sources, grounded in the full US patent corpus. We don't scrape proprietary databases. We don't license data we won't disclose. Every source is a real, established research database that your IP committee, your procurement team, and your scientists can verify.

Built in the open

AI ResearchPartner is a pre-launch product built by a small founder-led team. We'd rather tell you that plainly than manufacture social proof: there are no paying-customer logos on this site because we don't have permission to show any yet, and there are no testimonials because we won't invent them. What we have instead is the artifact — send us a topic and judge the dashboard on its citations.

Our co-development program is open for 2026: a small number of bespoke engagements for teams who want to shape the roadmap, with explicit phase gates and off-ramps if the output doesn't hold up. If that's you, email us.

Security & data

Analyses are stored against your account in our hosted infrastructure. Nothing is shared with third parties. Your research topics — including confidential disclosures — are not used to train external models, ours or anyone else's. You can purge any analysis from your account at any time.

On the Corporate R&D plan, SSO (Okta, Google Workspace) and audit logs are onboarding commitments scoped with your team, alongside a self-hosted deployment option. Email team@airesearchpartner.com for our security overview document.