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Users sign in through the app login screen. Brand users can be restricted to their own brand while admins can access broader reporting and user management.
AI Chatbot Reporting analyzes brand visibility inside ChatGPT, Gemini, and Perplexity answers, then turns those responses into dashboard metrics, recommendation intelligence, social/community visibility, discovered competitors, and content opportunities.
Built for SEO, content, marketing, brand, and reporting teams that need a repeatable way to measure AI visibility by theme, compare competitors, understand which brands are explicitly recommended, and decide what to improve next.
Instead of manually asking chatbots the same questions and copying answers into spreadsheets, the tool runs a repeatable audit across selected providers, themes, prompts, brands, competitors, and locations.
AI assistants are becoming research and recommendation engines. Brands need to know when they appear, how they rank, whether they are actively recommended, which communities are influencing answers, and where new competitors emerge.
SEO teams, content strategists, performance marketers, brand managers, agencies, and reporting teams tracking AI answer visibility.
Themes/topics, journey-stage prompts, brand mentions, rank, share of voice, citations, sentiment, discovered brands, social/community sources, and explicit recommendations.
Raw answers, parsed metrics, run dates, provider and theme context, recommendation/social/discovered-brand records, exports, and SQLite-backed reporting history.
Ranking and mention gaps, optimization opportunities, net-new content needs, suggested existing URLs, and competitor examples.
The current Express/EJS application flow covers login, Theme/Topic input, journey-stage prompt selection, report execution, and a dashboard that can be filtered by provider, run date, and theme.
Users sign in through the app login screen. Brand users can be restricted to their own brand while admins can access broader reporting and user management.
Add comma-separated themes/topics, brand name, optional alternate brand name, brand URL, location, and competitor names and URLs.
For every theme, the system generates three prompts in each of five journey stages. Users choose which questions should be included in the audit.
The backend checks ChatGPT, Gemini, and Perplexity, stores raw responses, parses tracked-brand metrics, and records discovered brands, social sources, and recommendations.
Filter by provider, date, and Theme/Topic, then review KPIs, recommendations, social/community visibility, opportunities, and the raw source answers.
The capabilities below reflect the current application routes, EJS screens, reporting logic, API endpoints, and SQLite schema.
Runs audits across ChatGPT, Gemini, and Perplexity with provider-specific reporting in the dashboard.
Starts with Theme/Topic inputs and generates 15 prompts per theme: three each for Broad Discovery, Qualification, Comparison, Validation, and Action/Decision.
Tracks the selected brand against configured competitors using brand names, display names, domains, and the generated input file.
Shows mentions, average rank, average share of voice, and citations in the main KPI cards, with sentiment available in its own reporting tab.
Filters metrics and detailed modules by Theme/Topic so teams can isolate visibility for a specific product, category, audience need, or strategic topic.
Surfaces untracked brands found naturally in AI responses, including mentions, average rank, expanded SOV, sentiment, citations, and prompt appearances.
Tracks social, community, and user-generated-content platforms such as Reddit, YouTube, Quora, and TikTok when they appear naturally in AI answers.
Separately tracks brands explicitly recommended in positions 1–3, including #1 wins, Top 3 appearances, average recommendation rank, recommendation share, and recommendation rate.
Highlights prompts where competitors outrank the brand or are mentioned while the selected brand is absent.
Classifies opportunities as Optimization or Net New and can include a suggested existing page plus an example competitor URL.
Stores exact chatbot answers by provider, theme, question, run date, and report brand, with search and TXT export from the dashboard.
Supports login sessions, brand-level access, run history, admin user management, and run controls for brand users.
These previews are recreated in static HTML/CSS so they can live safely on the public website. Labels, modules, filters, and layouts are based on the current EJS dashboard and input flow; demo values are intentionally generic.
The lightweight username/password login remains valid. After sign-in, users are routed to their permitted brand dashboard or input flow.
The old “keyword” wording is now presented as Theme/Topic. Users define strategic topics, brand details, location, and the competitors to track.
Each Theme/Topic receives three generated prompts in each of five stages. The first prompt in every stage is selected by default, and users can change the selection before creating the input file.
Theme/Topic: product comparison (MSV: 1,900)
The dashboard now combines Provider, Date, and Theme/Topic filters with run context, tracked competitors, execution statistics, and the four main KPI cards.
This module is separate from normal mention/rank metrics. It tracks brands that the LLM explicitly presents among its strongest choices and shows both the leading brands and your own recommendation performance.
| Brand | Status | Top 3 | #1 | Avg Rec. Rank | Rec. Share | Rec. Rate |
|---|---|---|---|---|---|---|
| Example Brand | Tracked | 12 | 8 | 1.42 | 38.7% | 40.0% |
| Competitor 1 | Tracked | 9 | 4 | 1.89 | 29.0% | 30.0% |
| New Brand | Untracked | 5 | 2 | 2.20 | 16.1% | 16.7% |
The tool identifies social/community/UGC platforms that appear naturally in AI responses. Social Visibility Share is intentionally calculated separately from normal brand SOV.
| Platform | Mentions | Avg Position | Social Visibility Share | Sentiment | Citations | Prompt Appearances |
|---|---|---|---|---|---|---|
| 11 | 1.8 | 42.6% | +12.4 | 5 | 8 | |
| YouTube | 7 | 2.3 | 28.4% | +8.1 | 4 | 6 |
| Quora | 4 | 3.1 | 17.2% | +3.5 | 2 | 4 |
| TikTok | 2 | 3.8 | 11.8% | +6.0 | 1 | 2 |
Beyond the configured competitor list, the report detects additional brands mentioned naturally by the LLM. These are measured separately so they do not alter the standard tracked-brand KPI calculations.
| Brand | Mentions | Avg Rank | Expanded SOV | Sentiment | Citations | Prompt Appearances |
|---|---|---|---|---|---|---|
| Emerging Brand A | 9 | 2.4 | 18.5% | +10.0 | 3 | 7 |
| Emerging Brand B | 6 | 3.2 | 12.9% | +4.0 | 2 | 5 |
| Emerging Brand C | 4 | 4.1 | 8.6% | 0.0 | 1 | 3 |
The optimizer remains an important output. It separates ranking and mention gaps, classifies each recommendation as Optimization or Net New, and can connect the opportunity to an existing page and competitor example.
| Prompt | Opportunity | Update Type | Suggested Existing URL | Example Competitor URL | Theme | MSV |
|---|---|---|---|---|---|---|
| Which options are best for buyers prioritizing quality and value? | Ranking | Optimization | /products/category-guide | /competitor/buying-guide | product comparison | 1,900 |
| What should buyers verify before choosing among these brands? | Mention | Net New | — | /competitor/reviews | customer reviews | 880 |
Users can search raw answers by theme, question, or answer text, reload the selected run, and export the response set to TXT for validation or downstream review.
| Theme | Question | Raw Answer |
|---|---|---|
| product comparison | How does Example Brand compare with leading alternatives? | Based on the criteria in the prompt, several brands stand out. Example Brand is frequently considered alongside... |
| customer reviews | What should buyers verify before choosing? | Buyers should compare warranty terms, independent reviews, community feedback, availability, and total cost... |
The current report combines executive-friendly KPI views with separate analytical modules and traceable raw responses, allowing teams to move from visibility measurement to specific content and brand actions.
Mentions, average rank, average SOV, citations, sentiment detail, total themes, total prompts, MSV, provider, date, domain, location, and tracked competitors.
Top 3 recommended brands, #1 wins, average recommendation rank, recommendation share, recommendation rate, tracked/untracked status, and your own recommendation summary.
Platform mentions, average position, Social Visibility Share, sentiment, citations, and prompt appearances for community and UGC sources found in answers.
Previously untracked brands, mentions, average rank, expanded SOV, sentiment, citations, and prompt appearances without changing normal tracked-brand KPIs.
Ranking gaps, mention gaps, optimization vs. net-new classification, suggested existing URLs, competitor examples, themes, and MSV.
Searchable raw responses, TXT export, provider/date/theme context, SQLite-backed run history, and generated output files for later review.
Runs repeatable prompt checks and structures the output automatically instead of relying on manual question-by-question testing.
Keeps tracked-brand KPIs, discovered competitors, social/community visibility, and explicit recommendation metrics distinct so each can be interpreted correctly.
Shows where competitors are winning and whether the next move should be page optimization, new content, stronger recommendation positioning, or community visibility work.
Theme filtering, stored raw responses, and run history make it easier to explain where a recommendation came from and compare visibility over time.
Sign in to run and review AI visibility reports across providers, themes, recommendations, social/community sources, discovered competitors, opportunities, and raw responses.