Earn the citations that LLMs trust
AI Citation Building that earns the third-party evidence LLMs reach for.
Generative engines weight citations differently than Google's PageRank. We engineer the references that earn you a seat inside the model's answer.
The problem
Domain authority is the wrong metric
LLMs cite niche publications, structured datasets, expert forums and community knowledge over generic high-DR sites. Classical link building optimizes for the wrong signal.
- Models cite evidence sources, not just popular sites
- Training corpora privilege specific publication clusters per domain
- Wikipedia, GitHub, Reddit and dataset references carry outsized weight
- Generic guest posting rarely earns AI citations
The solution
Engineered citation acquisition
We identify the citation surfaces that actually influence LLM answers in your category and earn placement across them.
Citation surface mapping
Identify the publications, datasets and communities models reach for in your category.
Evidence assets
Build original research, benchmarks and reference content worth citing.
Targeted outreach
Editor and contributor relationships across the mapped surface.
Community presence
Authentic, expert presence in the forums that feed model training and retrieval.
The process
How we run the program.
- 01
Citation audit
Reverse-engineer what's currently cited in your category by each major engine.
- 02
Asset planning
Plan the original assets needed to earn citations.
- 03
Production
Build the research, datasets and reference content.
- 04
Outreach & placement
Earn placements with editors, researchers and community leaders.
- 05
Measurement
Track citation lift and downstream AI visibility impact.
Benefits
What you walk away with.
- Higher citation density in generative answers
- Defensible authority that's hard for competitors to replicate
- Compounding lift across every other AI visibility workstream
- Brand presence in the contexts where buyers actually research
Deliverables
In the engagement.
- Citation surface map
- Quarterly evidence asset pipeline
- Outreach campaign execution
- Community presence program
- Citation lift reporting
Case study
Stratus Robotics
Industrial automation
Challenge
Strong product but invisible in technical AI answers — no authoritative third-party evidence in the model corpus.
Result
Published three benchmark studies and embedded experts in three communities. Cited in 58% of evaluated answers within 7 months.
+412
Earned citations
11% → 58%
AI answer inclusion
+193%
Inbound qualified leads
FAQ
Questions we get on AIC.
Is this the same as PR?
Overlapping but distinct. PR optimizes for headline reach. AI citation building optimizes for inclusion in the corpus and retrieval set that models actually use.
How is success measured?
By citation lift inside AI answers, weighted by prompt importance and competitor displacement.
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Learn moreReady to engineer your AIC program?
A 30-minute call with our team is the fastest way to size the opportunity and the plan.