An LLM Visibility Programme Built For AI-Native And Dev-Tool Brands
Get cited inside the models your technical buyers actually trust. This programme works both layers a model uses to source an answer: the corpus it trained on, and the publications it retrieves live.
Technical Buyers Read AI Answers On A Different Frame
Your buyers don’t take a recommendation at face value.
They check the source, weigh the publication, and look for primary research behind a claim. A model serving that buyer leans on the same signals. So winning the citation means earning coverage the model can stand behind, not just coverage that exists.
That’s the gap a generic brand mention programme misses, and the gap this one is built to close.
Two Layers Decide Whether A Model Cites You
Every large language model sources answers two ways. The strongest programmes build both at once.
The training-corpus layer
This is the durable library a model learned from. Coverage in authoritative, well-indexed publications builds the kind of standing that survives a model’s next training cycle.
The live-retrieval layer
This is what a model pulls in at answer time. Recent, specialist coverage in sources a model trusts to retrieve gets you cited on the assistants that lean on retrieval.
How The Programme Works Both Layers
Map your signal gaps
We test your category prompts across the major models and identify which layer you’re losing on, training, retrieval, or both.
Build durable and fresh coverage
We earn placements that strengthen the training layer over time and feed the retrieval layer with recent, specialist coverage.
Track citations by model
We report your citation share per assistant, so you can see exactly where the two-layer work is paying off.
What’s Included Every Month
Two-layer source construction
Coverage planned for both the training corpus and live retrieval, not one at the expense of the other.
Primary-source editorial
Placements in publications that cite research and carry weight with technical readers and the models that serve them.
Per-model citation tracking
A monthly read on where you’re cited across ChatGPT, Gemini, Perplexity and Claude, broken out by assistant.
A named technical strategist
One senior owner who understands developer and technical-buyer publishing, running your programme end to end.
Attributable reporting
Confirmed, traceable placements only. No impressions, no vanity metrics.
Competitive benchmarking
A clear view of which technical competitors the models cite, and where you can take the citation.
Your Programme Timeline, By Layer
Two-layer work pays off on two horizons. Here is what to expect, and when.
Weeks 1 to 2: Signal audit
We test your category prompts across ChatGPT, Gemini, Perplexity and Claude, then show which layer you are losing on, training, retrieval, or both.
Month 1 to 3: Retrieval wins first
Fresh, specialist coverage feeds the live-retrieval layer, so the assistants that retrieve on every answer start citing you first.
Month 4 to 6: Training layer compounds
Authoritative placements build the durable standing that survives a model retraining. This layer moves slower and lasts longer.
Ongoing: Per-model tracking
You get a monthly read on citation share per assistant, so you can see exactly where each layer is paying off.
Why Two-Layer Beats A Single-Layer Play
Most technical brands try one of these. Here is where each stops short of citations inside the models.
| Approach | Builds training layer | Builds retrieval layer | Per-model tracking | Done for you |
|---|---|---|---|---|
| LLM visibility programme | Yes | Yes | Yes | Yes |
| Generic AI mention work | Partly | Partly | Rarely | Yes |
| Technical content marketing | Slowly | No | No | Partly |
| AI tracking tool | No | No | Measures only | No |
| In-house DIY | Hard to sustain | Possible | Manual | No |
A tracking tool shows the gap by model. This programme is the source work that closes it.
Is LLM Visibility Right For You?
It is the deepest, most technical programme we run. It fits some brands far better than others.
A strong fit if
Your category is technical and your buyers read primary sources
You sell an AI-native product, developer tool, or technical platform
You are losing citations on careful assistants like Claude and Perplexity
You can invest in compounding, multi-month source work
Start elsewhere if
You are growth-stage but not deeply technical, see AI Brand Mentions
You are a product-led software team, see SaaS Brand Mentions
You are pre-revenue, start with Startup Visibility
You only need measurement, a tracking tool will be cheaper
LLM Visibility Questions
It’s a done-for-you programme that gets your brand cited inside large language models like ChatGPT, Gemini, Perplexity and Claude. It works two layers at once: the training corpus a model learned from, and the live sources it retrieves at answer time.
Some models lean on what they were trained on. Others retrieve fresh sources on every answer. If you only build one layer, you win citations on some assistants and lose them on others. The programme builds both, so you show up regardless of how a given model sources its answer.
AI-native products, developer tools, and technical B2B brands whose buyers evaluate carefully and read primary sources. If your category is technical and your buyers are engineers or analysts, this is built for you. Many SaaS and cybersecurity teams fit this profile.
The flagship programme builds broad multi-platform authority for growth-stage B2B. The LLM visibility programme goes deeper on the technical signal layer, with source construction tuned for how models weight primary research and specialist publications.
Editorial placements start landing by Month 3, with first measurable movement in tracked model queries by Month 4. The training-layer work compounds over a longer horizon, which is why the programme is built as an ongoing engagement, not a one-off.
Comparing programmes? See the solutions overview or pricing.
See Where The Models Cite You Today
Get a free audit. We’ll show you which layer you’re winning, which you’re losing, and what the LLM visibility programme would change.