How Ninar AI Works

Ninar AI is an AI search visibility platform. It measures how supported AI engines mention, position, cite, and describe a brand, then helps teams improve the evidence those engines can find.

1. Define the brand and buyer questions

Provide the brand, website, category, audience, and optional market. Ninar builds buyer-intent prompts appropriate to the selected workflow. Geographic scans distinguish remotely available software from businesses that must be physically local.

2. Run an engine-specific diagnostic

Ninar sends prompts to the engines available for the workflow and plan. Reports disclose the measured engine or surface, capture method, completed and unavailable prompts, and relevant configuration. API-generated diagnostics are not presented as consumer-application captures.

3. Score only completed evidence

The visibility report measures observed brand mentions, textual prominence, supporting citations, and sentiment. Failed or unavailable prompts are disclosed and excluded from completed-response denominators. Competitor extraction and competitor verification are tracked separately.

4. Identify visibility gaps

Ninar highlights buyer questions where the brand is absent, sources that influence answers, category confusion, weak product evidence, and verified alternatives. Use the comparison library to review Ninar against AI visibility and adjacent platforms.

5. Create and publish evidence

The AEO content workflow can produce structured FAQs, product explanations, comparisons, and supporting schema. Teams review content before publishing it through a connected WordPress site, universal embed, or their own publishing workflow.

6. Recheck with a comparable scan

Later scans can track changes when the engine, model settings, prompt set, geography, and scoring version are comparable. Cross-engine differences are labeled as comparisons, not performance trends.

Machine-readable resources

Run a free AI visibility audit or review the AI visibility software buyer guide.