SEO Explorer
A fast planning surface for turning a seed domain or topic into keyword ideas, intent filters, difficulty views, and recommended next-step tools.
Open Methodology
How SearchIQ turns search data into ranking action.
SearchIQ is built around a simple operating model: collect the right search signals, make them comparable, score opportunity, and route every insight into a clear next step.
1
Collect search signals
SearchIQ accepts uploaded files, Google Sheets, and BigQuery tables so teams can bring keyword, URL, anchor, SERP, and content data into one workspace.
2
Normalize intent and context
The platform maps inputs into practical fields like source, target, anchor, topic, URL, query, and metric columns before any tool starts its job.
3
Score opportunity
Each module turns raw rows into action signals such as semantic similarity, keyword priority, ranking visibility, and content opportunity.
4
Route the next action
Outputs are staged as downloadable result tables, job history, Slack alerts, and follow-up tool actions inside the dashboard.
Modules
The dashboard is a connected search workflow.
Each tool can stand alone, but the product is designed so research, semantic mapping, linking, and ranking inspection feed into one another.
Embedding Analyser
Compares text pairs in vector space to surface semantic relevance, matching strength, and clustering confidence across content sets.
Synapses
Links source, anchor, and target datasets to identify internal linking opportunities and pages that naturally reinforce each other.
Keyword Planner and KIQ
Expands seed terms, metadata, and page inputs into structured keyword opportunities, synonyms, and prioritised planning rows.
Anchor Text Generator
Suggests context-relevant anchor text recommendations that keep link language natural while improving topical clarity.
SEO Clarity
Tracks SERP visibility and ranking outputs so teams can inspect movement, coverage, and competitive search positions.
Governance
Measured usage, visible status, uninterrupted tools.
The billing ledger shows current-month token usage per user, while operational alerts help teams react early. Crossing a usage threshold is informational, so users can keep working.
Keep every run attributable to a user, source, tool, and job.
Use estimated token ledgers now, then accept actual model counts as tools report them.
Never expose saved Slack webhooks back to the browser.
Notify teams when usage needs attention without blocking workflow.