Getting Started with PingAura AI

PingAura Visibility Engine captures how your brand is referenced inside ChatGPT, Claude, Gemini, and Perplexity answers. This guide distills the product flows, what the backend reports, and the first actions that accelerate your visibility in agentic commerce.

1. What the product tracks

• Answer coverage: The engine scores how often your brand appears among the top-ranked LLM responses (see backend/docs/architecture/SCORES.md for details on the scoring model).
• Citation quality: Each mention logs whether it cites a canonical source from your landing pages, help center, or documentation.
• Sentiment and persona tags: Growth Team and Enterprise editions enrich each mention with persona intent and sentiment signals, which informs the dashboards described in backend/docs/features/SCORING_AND_METRICS.md.

The Visibility Engine converts passive AI chatter into measurable outcomes so you can prioritize the answers that get consumed by billions of buyers.

2. Get access

1. Request an invite at https://www.pingaura.ai; we provision a workspace per brand portfolio.
2. Authenticate with your work email, verify the workspace slug, and connect the Supabase-backed dashboard.
3. Define the brands, SKUs, and competitor sets you want PingAura to monitor—each becomes a “collection” visible across dashboards.

3. Connect sources and backend services

• Add the URLs you want AI models to cite (landing pages, product sheets, release notes). They feed into the crawler described in backend/docs/features/CLIENT_DOMAIN_INTEGRATION.md.
• Sync your knowledge base or help center so visibility data can point to actionable help text.
• Configure competitor domains and topic tags so the scoring engine (see backend/docs/architecture/ARCHITECTURE.md) can benchmark share-of-visibility per persona or region.
• Connect GA4 and GSC to attribute AI-driven sessions and queries back to visibility and citations.

4. Explore dashboards and alerts

• Visibility tab: Shows AI mention volume, platform share (ChatGPT vs. Gemini), and scoring breakdowns. Compare metrics across time windows.
• Citation tab: Tracks citation rate, share, and top cited URLs so you can see what sources models trust.
• Insights tab: Surface sentiment lift, persona-level performance, and geo visibility. Use filters to highlight the best answers for each region.
• Alerts & Playbooks: Enable Growth Team alerts for dips in coverage and activate agentic commerce playbooks to launch guided checkout flows inside AI agents.

5. Generate content and prompts

• Define your brand persona and preferred tone so articles and prompts match the voice buyers expect.
• Build prompt libraries by persona, region, and intent to standardize the questions the Visibility Engine runs.
• Maintain competitor lists to track who wins citations and mention share for each query cluster.
• Generate new articles or FAQs, then publish them to the channels you want models to cite.

6. Coordinate and act

• Share dashboards with product, marketing, and commerce teams so each insight has an owner.
• Combine Visibility Engine exports with Supabase analytics to measure conversion lift after visibility improvements.
• Launch persona-scale activation agents to convert AI conversations into meetings or purchases.

By following these product flows you turn the Visibility Engine from a monitoring tool into a revenue-driving asset for PingAura.ai.