Commercial Intelligence
Build your own AI visibility dashboard
Third-party AI visibility tools are useful when a team needs a fast baseline. They are weaker when leadership needs one commercial view across AI visibility, search demand, analytics, CRM, pipeline, and revenue.
Short answer
Use third-party tools as data feeds. Use APIs, exports, or connectors to bring the evidence into a controlled dataset. Use an LLM to interpret that prepared evidence with row-level references and clear limits.
What the dashboard should contain
The commercial question is whether buyers are finding, trusting, shortlisting, and converting through the official journey. A tool can show mentions, citations, prompts, traffic estimates, or share of voice. The company still needs to connect those signals to its own buyer data before treating them as evidence for a revenue decision.
AI visibility
Prompts, engines, countries, languages, brand mentions, competitor mentions, cited pages, cited domains, answer dates, and response snapshots.
Search demand
Search Console queries, landing pages, impressions, clicks, positions, and buyer-question clusters.
Commercial data
CRM leads, lead source, opportunity stage, deal value, sales cycle, conversion, lost reasons, and revenue.
Public proof
Reviews, directories, partner pages, third-party mentions, case studies, product feeds, service pages, and structured data checks.
API and export sources
The build should start with the access path before dashboard design. For each tool, record whether the data arrives through API, CSV export, Looker Studio, scheduled report, or manual upload.
- Ahrefs Brand Radar is useful for API-led AI response, cited page, cited domain, overview, and history data where the account has access.
- Otterly.ai is useful for prompt, citation, raw response, PDF, CSV, JSON, and Looker Studio workflows where the plan supports them.
- Semrush is useful when AI visibility, SEO, audit, keyword, and reporting data can be exported or accessed through available Semrush APIs and connectors.
- Similarweb is useful when AI visibility, traffic, referral, market, audience, and competitor data can be pulled into the company's own reporting layer.
- Google Search Console, analytics, CRM, and sales reporting are essential for first-party search, behaviour, pipeline, conversion, and revenue context.
How the LLM should be connected
The LLM should be the interpretation layer. It should receive a prepared evidence pack with fields such as date, prompt, engine, country, brand, competitor, cited URL, cited domain, query, landing page, lead source, opportunity stage, and revenue outcome.
- Summarise only from the supplied rows.
- Cite row IDs or source files for every major claim.
- Separate observed movement from interpretation.
- Flag missing data and leave gaps open.
- Show which commercial decision the evidence supports.
This allows a founder, CEO, commercial leader, or marketing team to ask sharper questions: which buyer prompts exclude us, which sources keep being cited, where competitors have stronger corroboration, which pages should be improved, and whether visibility movement is matched by better demand or sales outcomes.
When to build
Build the dashboard when the commercial problem crosses tool boundaries. That usually happens when leadership is comparing vendor dashboards, Search Console, analytics, CRM, sales notes, and revenue reports manually.
- The company sells across several markets or brands.
- AI visibility needs to be joined to real pipeline and revenue.
- Vendor interfaces disagree with first-party data.
- The team needs repeatable board or leadership reporting.
- The commercial question is about buyer confidence, channel leakage, official purchase path, or sales conversion.
Sources and related reading
FCP uses official vendor documentation where available, then separates those facts from commercial interpretation. Useful starting points include Ahrefs Brand Radar API documentation, Otterly API documentation, Semrush API guidance for AI visibility, Similarweb Gen AI Intelligence API documentation, the FCP AI visibility tools comparison, and the individual tool assessments for Semrush, Ahrefs, Similarweb, and Otterly.ai.
The dashboard should answer a commercial question before it becomes a reporting project: where is the buyer losing confidence, which public evidence is missing, and which sales or revenue movement proves the issue matters?