In active development · dogfooding on live client data

Find the pages
you're missing.

Cocoon reads your Search Console data, clusters it by what actually ranks together, and surfaces the new landing pages worth building — without cannibalizing the ones you have.

Request early access See the pipeline
1 GSC source of truth
2 Serpstat expansion
3 SERP validation
4 Clusters + cocoon
How it works

Two engines over one keyword pool.

SERP overlap — not intent — decides what belongs together. Intent is metadata. The expensive SERP checks only run on representative keywords, so a million queries cost a few thousand lookups.

🧭

Traditional clustering

Groups queries that should live on one page, by how much their search results actually overlap. Picks the main keyword and the URL that should own it.

SERP-overlap driven
🕸️

Semantic cocoon discovery

Runs regex and long-tail patterns on the raw keyword pool to find child-page candidates — jurisdictions, segments, questions, comparisons — your parent page is silently absorbing.

new-page finder
🛡️

Cannibalization guard

Before recommending a page, it measures overlap with the parent and existing URLs. High overlap? Keep it merged. Clean separation? Build it.

parent-separation score
The output

Every keyword gets a verdict.

Each cluster resolves to one decision — exportable to CSV, XLSX or JSON, with suggested URL, title, H1 and internal-linking anchors.

CREATE_CHILD_PAGE

Real demand, no page that owns it, clean separation from the parent.

ASSIGN_TO_EXISTING

A live URL already fits — route the keywords there instead of splitting.

KEEP_IN_PARENT

SERP says it's the same page — folding it in beats fragmenting.

MANUAL_REVIEW

A page exists but the parent steals its traffic — a human should decide.

1M+
GSC rows analysed per project
~5k
SERP lookups, not a million
4
decision states, zero guesswork
60+
jurisdictions in the cocoon dictionary