Case study · Technology
Engineered variants, named in most AI answers
How premium wire distribution with several engineered release variants multiplied a new programme's indexed footprint and its presence in AI answers.
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An advanced technology programme needed to be found by applicants asking AI where to study. Engineered variants multiplied its indexed footprint, and the engines named it in most answers.
- How several release variants widened the questions it could answer
- Brand and category results across five AI engines
- Which messages the engines adopted
- The competitive set the engines return, and the next cycle
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The objective
Be named when prospective applicants ask AI assistants about programmes in the category, within weeks of the announcement.
The route, and why
Premium distribution across the three national wires, with several engineered variants of the release, each angled to a different applicant question.
What the engines said
On brand questions, every engine returned the programme and cited the distributed coverage, and the client held more than half of every name the engines returned. On open category questions it was named in about two of three answers, and one engine made it the headline answer.
Of the messages tracked, the admissions route and the career framing travelled furthest. The primacy claim travelled least, which shaped our advice for the next release.
The honest gaps
On broad category questions the engines list seven to eleven institutions per answer, and the client held about one in nine of those mentions.
What we recommended next
Lead with the messages that travelled, and target the specific competitor set the engines return, through organic features and comparison coverage.
Results
- 609verified pickups, 53 premium placements across 41 brands
- ~10xindexed footprint of a single-version run
- 80%of all AI observations named the client
- 87%of AI-chat answers named it
- 55%share of names on brand questions
- 58%of tracked messages adopted
| Measure | Result |
|---|---|
| Verified pickups, 53 premium placements across 41 brands | 609 |
| Indexed footprint of a single-version run | ~10x |
| Of all AI observations named the client | 80% |
| Of AI-chat answers named it | 87% |
| Share of names on brand questions | 55% |
| Of tracked messages adopted | 58% |
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