UNI.01
Research Grant Writing
Proposal drafts built from researchers’ own outputs and checked against Horizon Europe, ERC, UKRI and NIH rules, with AI use logged for disclosure and the author signing.
Open pageIndustry
AI for researchers,
with receipts.
Cited, reproducible, on your infrastructure. Grant writing, systematic reviews and research data management for universities, institutes and research offices. Built on your own repositories and infrastructure, with every claim cited and the researcher as author.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI for researchers is most valuable on the work around the research: assembling grant applications, screening thousands of abstracts for a systematic review, and describing datasets so they can be found and reused. It helps only when every claim is cited, every step is reproducible and the researcher stays the author.
Where it breaks today
01
Applications eat research time
Researchers spend weeks on proposals with uncertain odds, and research offices re-explain the same funder rules and templates every round, by hand.
02
Reviews take longer than the field moves
A systematic review can take long enough that new studies appear before it is published. Most of that time is screening and extraction, not thinking.
03
Data is kept, not findable
Funders require data management plans and FAIR data, but metadata is thin, files sit on shared drives and the dataset behind a paper is hard to locate five years on.
OUR APPROACH
Institutional systems, not another chat tab
The best AI tools for researchers on a personal level already exist, and many of your staff use them. What an institution cannot get from a personal tool is a system that knows its own repository, its funder templates and its data policies, that runs where unpublished work and personal research data are allowed to go, and that leaves an audit trail a research integrity office can read.
That is what we build. Research integrity frameworks and funder rules increasingly ask researchers to disclose and take responsibility for AI use; we design for that, with citations to source, logged steps and drafts the researcher edits and owns. Where personal data is processed for research, GDPR Article 89 safeguards and your ethics approvals set the limits, and that assessment is your data protection officer’s.
HOW IT DEPLOYS
PHASE 01
Start with one office or group
A research office, a library review service or one lab: a defined user group with a measurable workload.
PHASE 02
Connect your sources
Your repository, CRIS, funder templates and reference databases, on institutional or sovereign cloud infrastructure.
PHASE 03
Evaluate before rollout
Recall and citation accuracy measured against work your researchers have already done by hand.
What we build
01
Research Grant Writing
Drafting and compliance support for Horizon Europe, ERC, UKRI, NIH and national calls, built from the researcher’s own outputs and the funder’s rules.
SEE GRANT WRITING →02
Systematic Reviews
Screening and data extraction with dual human review preserved, recall measured, and a PRISMA-ready record of every decision.
SEE SYSTEMATIC REVIEWS →03
Research Data Management
Metadata drafted from the files themselves, data management plans kept current, and datasets made findable to FAIR principles in your repository.
SEE DATA MANAGEMENT →04
Institutional Knowledge Search
Search across theses, reports and publications in your own repository, with answers that cite the document and page they came from.
SEE RAG SYSTEMS →Controls we implement
UNPUBLISHED WORK ON YOUR INFRASTRUCTURE
CITATION ON EVERY CLAIM
NO TRAINING ON YOUR DATA
LOGGED, REPRODUCIBLE STEPS
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and research integrity and data protection assessments remain your institution’s — see our trust centre.
Research institutions use cases
-
-
UNI.02
Systematic Reviews
Screening prioritisation and source-linked extraction for systematic reviews, with recall measured, dual human review kept and a PRISMA 2020-ready record of every decision.
Open page -
UNI.03
Research Data Management
Metadata drafted from datasets, data management plans kept current and personal data flagged before deposit, making repositories FAIR with a steward approving each record.
Open page
Questions
Questions about Universities & Research Institutes.
What are the best AI tools for researchers?
For individual work, research assistants that search and summarise the literature with citations, reference managers with AI features and general models for drafting all have a place. The limits appear at institutional scale: unpublished data, personal research data, your own repository and your funders’ rules. That is where a system built on your infrastructure and sources does what a personal tool cannot.
How can universities use AI in research?
The most dependable uses are around the research rather than in its conclusions: drafting and checking grant applications, screening and extracting studies for reviews, describing and cataloguing data, and searching the institution’s own outputs. Each keeps the researcher as author and decision-maker, with citations and logs that a research integrity office can audit.
Can AI tools for researchers be trusted with citations?
Only if the system is built so that a citation must resolve to a real passage before it is shown. General chat models can invent plausible references. We build retrieval over defined sources, check each citation against the stored text and drop any sentence whose source does not resolve, then measure citation accuracy on your own material.
Does using AI in research comply with GDPR?
It can, if the processing has a lawful basis, research safeguards under Article 89 are in place and personal data does not go to providers without processor terms. Running models on institutional or EU sovereign infrastructure simplifies this considerably. The data protection assessment is your institution’s to make; we design the architecture to support it.
What do research funders say about generative AI?
Policies vary and change, but the common thread is that applicants remain fully responsible for their proposals and must not breach confidentiality, and that peer reviewers should not upload applications to AI tools. Some funders ask for disclosure of AI use. We build drafting support that keeps the researcher’s own work at the centre and logs how AI was used.
