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Turn pages into meaning.

See what a site is really about. Pages group by topic on their own, with issues mapped right where they live.

From page content to a true index of the site.

Silkra turns what every page says into vectors, locally, as it scrapes. That index is what search, groups, and overlap all read from.

  1. 01

    Read the page

    Silkra pulls the main content from every page it scrapes and splits it into passages, the same way a language model reads.

  2. 02

    Embed it on your machine

    A built-in model turns each passage into a vector inside the app. No API key, no per-page fee, nothing uploaded.

  3. 03

    Build the index

    Passages roll up into one vector per page, so every URL sits in the index next to pages that mean the same thing.

Visualize meaning on the map.

The same vectors sort every page into a topic, then put the findings right on those groups, so you can see what a site covers and where it falls short.

Get started

A semantic foundation

Spot cannibalization easily

Two URLs can sit in the same space. The map flags that overlap so you can decide whether to consolidate, differentiate, or leave them apart.

See pages that never link

Find pages that do not link to their most similar counterpart. Those pages light up on the map so you can add the connection or leave them as they are.

Group the outliers

Find pages with no semantic group, plus the ones that sit off-topic from the rest of the site. Those outliers surface so you can rework them or get rid of them.

Filter and find similar content.

Search by what pages say, not just the words they use. The Silkra agent and any AI tool you connect query the same index.

The agent finds what keywords miss

Describe what you're after and the agent searches every page by meaning. It finds the ones that answer the question, even when they never use your phrasing.

Your AI tools know what's covered

An LLM usually has no idea what's already in the library, so its topic ideas repeat old work. Over MCP, Claude or ChatGPT searches the site by phrase first, then suggests ideas that build on what exists.

How a page gets its meaning

What is semantic analysis for SEO

Chunks, then a page-level centroid

A language model does not read a URL as one blob. It breaks the page into passages, or chunks, and works from those. Silkra does the same as it scrapes: it splits the page, embeds each chunk behind the scenes, and keeps those vectors with the URL.

Those chunk embeddings are then added together into a page-level centroid. Summing the parts gives a stable reading of what the whole page is about, not just the title or a keyword. That is the meaning the map, the search, and the table all share.

Cosine is a comparison, not an answer

Cosine similarity is how large language models decide two pieces of text are about the same thing. It is a distance between meanings. It is not something they use when they write an answer.

Silkra uses that measure across the crawl so related pages, competing pages, and leftovers are visible on the site you already have. Working from relational meaning and topic coverage is a powerful strategy: consolidate the overlap, connect pages that belong together, and rework or remove the outliers. That is the practice we want SEOs to have.

Create your first workspace.

Crawl a site, then ask what needs attention.

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Free to start. No credit card needed.