Original research for AI is data your team produces—surveys, benchmarks, experiments, or analyses—structured so that AI platforms can confidently reuse and cite it. When paired with transparent methodology and clear visuals, original research becomes a durable citation asset.
Why original research wins citations
- Uniqueness: Novel data fills gaps that AIs can’t source elsewhere.
- Credibility: Methods, sample sizes, and limitations signal trust.
- Reusability: Charts, tables, and headline stats are easy to quote.
What to publish
- Annual/quarterly studies with stable categories so time series are possible.
- Benchmarks that compare tools or techniques with defined criteria.
- Datasets with documentation and licensing for reuse.
How to present
- Methods section: Sampling, collection dates, instruments, exclusion criteria.
- TL;DR: 3–5 headline findings; include a chart or table.
- Semantic chunking: Short sections; questions as subheads; FAQs.
Measuring impact
- Citation frequency for research URLs by platform.
- AI brand mentions tied to research headlines.
- Branded search lift following publication.
How we run the program
We plan one flagship study per year with stable categories so we can compare year over year and publish two to three focused benchmarks in between. Before fielding a survey we draft the questions as they would appear on the final page with headings, a TLDR, and at least one chart per finding. We predefine how we will segment the data so we do not overfit later.
After publication we seed highlights to trusted hubs and pitch two or three editors with one precise quote or data point each. We annotate the publication date in our dashboard and tag prompts that align with the findings so we can see when and where the study appears in citations and whether phrasing in answers begins to echo our language.
How our product helps
We tag prompts with research themes, track pickup across platforms, and run link citation audits to find derivative citations. We compute position weighted citation share so we can tell whether the study is informing the core of answers or appearing as a late reference. We export a monthly view to keep stakeholders informed and feed the next round of editorial planning.