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PingAura Visibility Engine for Agentic Commerce

A product-level overview that explains how the Visibility Engine captures AI mentions, scores them, and turns those insights into an AI Engine Optimisation strategy.

PingAura Visibility Engine is a single source of truth for brands that want to be chosen before a customer ever visits a website. The engine watches ChatGPT, Gemini, Perplexity, and other agents, turns their answers into measurable signals, and delivers that intelligence to CMOs who are building AI Engine Optimisation (AEO) programs.

1. Agentic commerce is the new battlefield

Agentic commerce replaces ecommerce’s menu of choices with contextual conversations. As our blog explains in What Is Agentic Commerce?, AI agents now:

  • Answer questions (ask → decide → buy) inside one conversational loop instead of sending buyers through ten pages.
  • Personalise every answer by persona, geography, and moment-in-time intent.
  • Compare thousands of variables instantly so the brand that shows up first owns the narrative.
  • Will soon host native ads and transactions, making AI visibility the gating factor for cheaper acquisition and faster conversions.

The Visibility Engine turns this new reality into data: which answers mention you, how they cite your content, what persona they match, and how often competitors steal the top slots.

2. How the Visibility Engine works

  • Multi-tenant data model (FastAPI + PostgreSQL + RLS) keeps each brand’s history and prompts isolated while offering package-based feature gating (Starter, Professional, Enterprise).
  • LangGraph workflows orchestrate answer crawling and scoring. A first graph fetches answers from OpenAI, Perplexity, Gemini, then queues a second scoring graph that calculates metrics and refreshes aggregates.
  • Redis streams + background workers separate the API from slow LLM calls, ensuring dashboards update without blocking users.
  • Sentiment and hallucination signals run in parallel (DistilBERT, spaCy) so you can understand not just volume but tone.
  • Circuit breakers, retries, and observability instrumentation protect the system when models misbehave, keeping scores honest even during AI outages.

See backend/docs/architecture/ARCHITECTURE.md for a deeper walkthrough of the stack and backend/docs/features/SCORING_AND_METRICS.md for the exact formulas that calculate GEO and sentiment scores.

3. Signals you can act on

  • Presence, Position, Share, Penalty: The Visibility Score blends whether your domain appears, where it appears in the answer, how much of the citation share you own, and subtracts a penalty when hallucinations slip through. Scores are clamped between 0–1 so you can benchmark across categories.
  • Citation rate, unique sources, citation share: Track how often answers cite your canonical URLs, how many distinct domains are referenced, and whether you’re winning the citation share versus competitors.
  • Sentiment breakdowns: Each answer is tagged with sentiment (positive/neutral/negative) and persona intent so you can route PR, product, or support actions accordingly.
  • Prompt-level trends: Understand which questions trigger buying intent, whether specific personas see your brand, and how geography shifts the story.

All of these metrics feed the dashboards that brand teams share across product, marketing, and commerce, letting each team own a slice of AI visibility.

4. AEO in practice — bring the blueprint to life

AI Engine Optimisation is the playbook our blog AI Engine Optimisation Strategy Blueprint for CMOs lays out:

  1. Visibility diagnosis — Understand your Share-of-Voice, sentiment, citations, and prompt-level positioning across ChatGPT, Perplexity, and Gemini. The Visibility Engine delivers these dashboards out of the box.
  2. Content & narrative correction — Publish LLM-friendly content (structured FAQs, comparison sheets, fact sheets) so AI models cite your sources instead of inventing wrong details.
  3. Competitor & category mapping — Identify who AI recommends instead of you, which prompts lead to conversion, and which citation domains dominate the space.
  4. AEO content optimisation — Build semantically rich, persona-aware content, strengthen digital authority, and feed it directly into the Visibility Engine’s prompt library.
  5. AI Ads, sponsored answers & agentic actions — Prepare for AI-native monetization by maintaining a high-quality citation footprint, which lowers ad costs once platforms start inserting paid placements or built-in checkout links.
  6. Zero-click transactions — Align your product pages, knowledge base, and fulfilment systems so the AI stays on message even when it completes the transaction without a website hit.

5. Next steps for brands

  • Connect the Visibility Engine to your brand’s domains and knowledge base to power citation-quality insights.
  • Share dashboards with marketing, product, commerce, and growth to accelerate improvements in the prompts that matter most.
  • Use Visibility Engine exports to measure conversion lift, then feed that evidence into stakeholder reviews.
  • Treat AI visibility like a new channel: keep your narratives fresh, monitor competitor shifts daily, and move faster when penalties rise.

PingAura Visibility Engine is the foundation for AEO, giving brands the instrumentation, signals, and strategy to dominate agentic commerce.

6. Read the blog for the full stories