MyPhotoAI · pSEO case study

The minimum unique content threshold for a pSEO leaf, with measurements

The most common reason a pSEO surface fails to index is that the leaves are not different enough from one another. Substituted-noun templates trigger Google's auto-generated-content classifier; the symptoms are coverage stagnation at twenty to thirty percent and slug-diversity scores under five on the audit tool. This leaf documents the unique-content floor we hit on every leaf we ship and the test we run before publish.

By AlkenaCode engineering team

We ship programmatic SEO surfaces, AI-driven receptionists, and case-study sites for Kenyan SMEs and overseas clients. Every claim on this page traces back to shipping work, not speculation. The audit tool on /audit is the same software we use to evaluate prospective engagements.

Authors of the myphotoai.alkenacode.dev pSEO surface itself. Source code is open at github.com/Kiragu-Maina/alkenacode-family. Production deployments include agents.alkenacode.dev and home.alkenacode.dev.

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The 1500-character floor on this surface is enforced by content/slug-validator.ts. The cosine test is a separate Python script in scripts/check-similarity.py we run before each batch of new leaves; it has rejected four drafts on this very surface during authoring.

The minimum unique content threshold for a pSEO leaf, with measurements

The 1500-character body floor

Our build-time validator rejects any leaf whose body sections sum to fewer than 1500 characters. The floor was set after we observed indexing failures on two surfaces where the average body sat around eight hundred characters; bumping the floor to fifteen hundred pulled coverage from thirty percent to seventy-eight percent over thirty days. Fifteen hundred is the floor; the average on this surface is closer to twenty-two hundred. Words per leaf matter less than substance per leaf, but the byte count is the proxy the validator can measure cheaply.

The cosine-similarity pre-publish test

Before publishing a new leaf we run a cosine-similarity test against the existing leaves using simple TF-IDF vectors over the body text. Any pair scoring above 0.72 gets flagged for an editorial rewrite; pairs above 0.85 block the publish. The threshold of 0.72 was set empirically: leaves with a peer in the 0.72 to 0.85 band reliably indexed but their per-page impressions stayed flat; leaves below 0.72 grew their impressions over time. The test is forty lines of Python and runs in under five seconds across this entire surface.

Where the uniqueness has to come from

Numerics, examples, and arguments are the only durable sources of uniqueness. Re-skinning the same paragraph with synonyms does not pass the cosine test, does not pass a quality rater, and will not rank. Numerics that are specific to your shipping work are the cheapest source; this leaf has two such numerics, the 1500-character floor and the 0.72 cosine threshold. If you cannot find at least two specific numerics per leaf to anchor the writing, the topic is too thin for pSEO. Editorial is the better play.

src/app/p/[slug]/page.tsxtsx
import { SLUGS } from "../../../../content/slugs";

export const dynamicParams = false;

export function generateStaticParams() {
  return SLUGS.map((s) => ({ slug: s.slug }));
}

export default function LeafPage({ params }: { params: { slug: string } }) {
  const leaf = SLUGS.find((s) => s.slug === params.slug);
  if (!leaf) notFound();
  // ... render the leaf
}
The leaf route's generateStaticParams reads the manifest at build time and tells Next.js which slugs to emit as static HTML.

Sources

Every claim on this page traces back to one of the verifiable sources below. Citations are publisher-stable; we avoid blog posts and other URLs that may not survive a year.

  1. Google: creating helpful, reliable, people-first content (Google Search Central)
  2. Google Search Quality Rater Guidelines (PDF) (Google Search Central)
  3. Google: spam policies for Google web search (Google Search Central)
  4. Google: spam policies, auto-generated content (Google Search Central)
  5. Google: consolidate duplicate URLs (Google Search Central)
  6. Google: indexing overview (Google Search Central)
  7. Google: managing crawl budget for large sites (Google Search Central)
  8. Google: title link and meta description best practices (Google Search Central)
  9. web.dev: Lighthouse SEO audits (Chrome for Developers)
  10. Google: sitemaps overview (Google Search Central)
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