Own every LLM app search your data can answer
LLM app sits inside ai, ml & data platforms, and inherits its search physics — but not its page set. AI platforms should be the best-cited entities in their own category, and most are not. For llm app specifically, the surface is narrower and far more defensible: the queries carry the niche modifier, the buyer already knows what they want, and the competing pages are usually category-level content that never names the niche at all.
Own the specific question before you contest the category head term.
- Addressable URLs
- 747,936
- Pass the index gate
- 31%
- Templates shipped
- 4
What goes wrong in llm app programmatic builds
Thought-leadership content about AI generally, which is the most commoditised text on the internet. If your page could have been written by a model with no access to your systems, it will be replaced by one. In a llm app build the trap is worse, because the addressable set is smaller: publishing the whole matrix regardless of data completeness leaves you with a thin cluster and nothing to consolidate into.
Where the llm app demand actually sits
Before anything is generated we rank the page families by intent, competitive difficulty and how complete your data is. Build order follows this table, not keyword volume.
| Page family | Representative query | Intent | Difficulty | Build priority |
|---|---|---|---|---|
Models /models/{model} | llm app pricing per token | Informational | Low | 100 |
Compare /compare/{model-a}-vs-{model-b} | llm app vs alternatives benchmark | Informational | Low | 94 |
Integrations /integrations/{framework} | how to use llm app with my framework | Informational | Medium | 80 |
Benchmarks /benchmarks/{task} | llm app context window limits | Commercial | High | 67 |
How llm app entities multiply into pages
Your addressable surface is not a keyword list, it is a set of entity axes taken from your own data. Multiply them and you get the theoretical maximum; the index gate decides how much of it deserves a URL.
53 model × 28 model a × 24 model b × 21 frameworkWhat fuels a llm app surface
Programmatic pages are only as defensible as the data behind them. These are the sources we ingest before a template is written.
Model versions, context windows, modalities, pricing, latency.
Hard numbers that change often and are searched constantly.
Reproducible evaluations with methodology.
Benchmarks with published methodology are heavily cited.
Working code paths per framework.
Developers search by framework, not by feature.
SoftwareApplication + DatasetModel and benchmark entities become machine-readable and quotable.
TechArticle for cookbooksDeveloper content is judged as documentation, which is what it is.
Organization with knowsAboutAnchors the company as a category entity in the knowledge graph.
- Benchmarks publish methodology, hardware and date, or they do not publish.
- Deprecated models keep pages with migration guidance — silent removal breaks trust and links.
- Capability claims are reproducible from the published harness.
The templates a llm app build ships
Each template answers a different question. If two templates would answer the same one, we consolidate instead of publishing both.
/models/{model}/models/wbp-embed-v2Capability and pricing lookup. Scoped to llm app, so the modifier appears in the URL, the H1 and the data behind it.
Model card with limits, pricing and latency.
The URL tree and the rules that gate it
Two things decide whether a scaled surface survives: how the URLs nest, and what stops a page being born when the data is not there.
- /Home — links to every hub, nothing below it is orphaned.
- /models/Hub for the models family — filterable index, links to every child.
- /models/{model}Model card with limits, pricing and latency.
- /compare/Hub for the compare family — filterable index, links to every child.
- /compare/{model-a}-vs-{model-b}Benchmark deltas with methodology.
- /integrations/Hub for the integrations family — filterable index, links to every child.
- /integrations/{framework}Tested code samples per version.
- /benchmarks/Hub for the benchmarks family — filterable index, links to every child.
- /benchmarks/{task}Reproducible eval results with dates.
IF unique_facts_from("Model registry") < 7SKIP — the URL is never generated. No page, no thin cluster, no cleanup later.
IF rows_from("Benchmark harness results") IS EMPTYRENDER parent hub instead and 301 the child pattern into it.
IF query_overlap(new_page, existing_page) > 0.7CONSOLIDATE — extend the existing URL rather than publishing a near-duplicate.
IF source_row.updated_at older than the refresh windowFLAG for regeneration; the page keeps serving but drops out of the priority sitemap.
IF schema fields cannot be filled from real dataOMIT the schema block. Markup never states something the visible page cannot.
IF page passes gate AND llm app guardrails clearPUBLISH into the next release tranche, not all at once.
Would this llm app page deserve to exist?
This is the actual gate we run before a URL is generated. Toggle what your page would have and watch the verdict change.
Borderline. A human reviews the sample page before the family ships.
Every llm app page we generate has to clear 80 before it enters the sitemap. That single rule is why these sets survive scaled-content reviews.
Everything shipped in a llm app build
Fixed scope, fixed price. You own the data contract, the templates and the pipeline at the end of the engagement.
A normalised schema across model registry, benchmark harness results, integration cookbook, with required fields, validation rules and the fill rate you need before generation starts.
One template per intent — /models/{model}, /compare/{model-a}-vs-{model-b}, /integrations/{framework}, /benchmarks/{task} — each with its own H1 logic, fact blocks and internal-link rules.
The scoring rule that decides which of the ~747,936 theoretical combinations become URLs. Typically 31% clear it on the first pass.
SoftwareApplication + Dataset + TechArticle for cookbooks + Organization with knowsAbout generated from the same source fields the page renders, so markup and content can never disagree.
Hub, spoke and sibling links generated from the data relationships, not hand-maintained menus — no orphans at any tranche size.
Tranche-by-tranche publishing with indexation checkpoints, so the surface grows at a rate Google's scaled-content systems read as normal.
Regeneration triggers tied to source-data changes, plus lastmod handling so recrawls are earned rather than requested.
Search Console segmentation per pattern, so you can kill an underperforming template instead of guessing at the whole set.
- You have no structured llm app data yet — no catalogue, registry or database to generate from.
- You want thousands of pages live this month. Every build here ships in tranches with indexation checkpoints.
- You need guaranteed rankings by a fixed date. Nobody can sell that honestly.
- You want pages written by a model with no fact source behind them — that is the exact pattern that gets sets deindexed.
Size a llm app programmatic surface
Defaults are conservative starting points, not promises. Change every field to your own numbers — the formula is shown so you can check it.
Defaults reflect developer-platform economics; substitute your own activation-to-revenue figures. Sized down to a specialist llm app operation rather than the whole category.
- Pages earning impressions
- 67
- Monthly organic clicks
- 804
- Monthly activated API accounts
- 22
- Monthly value
- $75,350
How this plays out in llm app
Delivery patterns from real builds, described by mechanism rather than by client name. We publish named results only with written permission and dated figures.
Pricing and limits only visible after signup.
Public model cards with pricing, context windows, latency and change history, marked up as structured data.
Assistants answering 'which model supports X' can cite you instead of a third-party roundup.
What happens after you book a call
- 1Ship the first tranche, wait for indexation data, then release the next — never all at once.
- 2Export the source data and profile it for completeness before a single template is drafted.
- 3Score the candidate intersections by demand, data completeness and commercial value; cut the bottom half.
- 4Write one page by hand, end to end. If it isn't genuinely useful, the template will not save it.
LLM app: straight answers
How many pages does a llm app build actually need?›
Fewer than most agencies quote. We size the first batch from your data completeness, not from a keyword export — for a llm app operation that is usually a double-digit set of fully supported pages, expanded in tranches once indexation data comes back.
Will these pages compete with our existing llm app pages?›
No. Before generation we map every existing URL to its query cluster; where a new template would overlap, we either consolidate into the existing page or change the template's angle. Cannibalisation is a mapping failure, not an inevitability.
What data do you need from a llm app business to start?›
Whatever you already run on: model registry and benchmark harness results. Phase one normalises it into a data contract; nothing is generated until each required field is populated.
Our pricing changes constantly.›
Feed-driven pages with as-of dates. Changing prices are a freshness advantage, not a liability.
Competitors will copy our benchmarks.›
They can copy numbers, not the reproducible harness or the citation history that comes with publishing first.
Want the LLM app surface scoped before you build it?
We'll audit the data source, size the first batch, set the performance budget and tell you honestly if programmatic is the wrong tool for your category.
