Foundiqo

Methodology

Foundiqo SEO + GEO audit methodology

Foundiqo scores whether a brand is crawlable, understandable, evidence-backed, comparable, offer-clear, and safe to prepare for AI answer systems and agents.

SEO Foundation

18%

Crawlability, metadata, indexability, headings, canonical, and public buyer pages.

AI Search Readiness

18%

Answer-ready facts, citation context, prompt coverage, and source clarity.

Discovery Readiness

12%

Whether a category, audience, use case, and value proposition are explicit.

Content Clarity

12%

Direct answers, page depth, buyer intent, FAQ blocks, and internal context.

Proof & Trust

12%

Claims mapped to official evidence, policy pages, freshness, and review state.

Structured Data

10%

JSON-LD, Organization, SoftwareApplication, Product/Service, FAQPage, and breadcrumbs.

Comparison Context

7%

Alternatives, competitor framing, best-fit/not-best-fit, and buyer tradeoffs.

Offer Readiness

7%

Pricing, trial tier, constraints, CTA, eligibility, support, and refund policy.

AI Action Readiness

4%

llms.txt, well-known files, MCP/action specs, approval guardrails, and monitoring setup.

Score Thresholds

0-39

Critical

Core crawl, clarity, proof, or offer signals are missing. Do not rely on SEO + GEO workflows yet.

40-59

Needs work

The brand can be evaluated, but major proof, schema, answer coverage, or offer gaps remain.

60-79

Good foundation

The brand has useful SEO/GEO signals and needs focused fixes before stronger monitoring.

80-100

GEO ready baseline

Signals are strong enough for ongoing monitoring, provider capture, and controlled publication.

Finding Model

Every finding should carry severity, evidence, affected page or asset, SEO impact, GEO impact, business impact, implementation fix, effort, owner suggestion, and validation step.

Critical findings block crawl, indexability, trust, or the main conversion path. High findings hurt AI understanding, citation confidence, or buyer-intent comparison. Medium and low findings strengthen completeness and monitoring quality.

Limitations

  • Scores are readiness indicators, not search ranking, AI citation, recommendation, conversion, or revenue guarantees.
  • Public audits use lightweight crawl evidence. Deep crawl and scheduled monitoring require domain verification.
  • Prompt outputs are provider or manual evidence snapshots and can change as AI systems update.
  • Generated schema, llms.txt, well-known JSON, reports, and snippets require human approval before publishing.
  • Competitor comparisons must be reviewed against current public vendor docs and pricing before procurement use.