CAMPAIGN AI VISIBILITY

The AI Ballot Problem

Voters are increasingly turning to AI systems — ChatGPT, Perplexity, Gemini, Grok — to answer questions about local candidates before they ever visit a campaign website. 'Who is running for city council in Polk County?' 'What does the school board candidate in Hillsborough believe about curriculum?' 'Who is the incumbent for Florida House District 42?' These questions are being asked millions of times across AI platforms, and the answers are being generated from whatever entity data exists in the AI's training corpus.

For most local candidates, that entity data is either nonexistent, incomplete, or worse — dominated by whatever their opponent's campaign has published. The candidate who builds structured entity infrastructure first wins the AI ballot by default. There is no competition because almost no local campaigns understand this yet.

BackTier's Campaign AI Visibility service is built specifically for this gap. We audit your candidate's current AI presence across all five major AI engines, identify every gap and narrative risk, and deploy the entity architecture that establishes your candidate as the authoritative answer for their race, their district, and their policy positions.

The Five-Layer Campaign Entity Stack

Every Campaign AI Visibility engagement begins with an entity audit — a systematic query of ChatGPT, Perplexity, Gemini, Claude, and Grok using your candidate's name, district, party affiliation, and key policy positions. The audit reveals what AI currently says, what's missing, and what your opponent's AI presence looks like. Delivered within 48 hours of engagement.

Layer one is the candidate entity architecture: Person schema with full political context — PoliticalParty affiliation, GeoCoordinates for the district, homeLocation, and a complete @graph JSON-LD block that tells AI systems exactly who your candidate is, what office they're seeking, and what they stand for. This is the foundation that every other layer builds on.

Layer two is voter Q&A optimization. We research the exact questions voters are asking AI about your race — policy positions, candidate backgrounds, voting records, endorsements — and build FAQPage schema that puts your candidate's answers in the AI response. Not your opponent's. Not a neutral summary. Your candidate's position, structured as the authoritative answer.

Layers three through five cover GEO content infrastructure, trust signal architecture, and citation network building. The result is a candidate who shows up in AI-generated answers with authority, accuracy, and consistency — across every AI engine, for every relevant voter query.

AI Entity Audit

We query every major AI engine with your candidate's name, district, and key issues. Full report of what AI currently says, what's missing, and what your opponent's AI presence looks like. Delivered in 48 hours.

Candidate Entity Architecture

Person schema, PoliticalParty schema, GeoCoordinates for your district, and a full @graph JSON-LD block that tells AI systems exactly who your candidate is, what they stand for, and where they serve.

Voter Q&A Optimization

FAQPage schema built around the exact questions voters are asking AI about your race. Your candidate's answers in the AI response — not your opponent's, not a neutral summary.

GEO Content Infrastructure

Long-form, EEAT-compliant pages that AI systems synthesize as authoritative sources. Policy position pages, candidate biography, district service record. Built to be cited, not just indexed.

Ongoing Monitoring

Continuous monitoring of AI-generated answers for your candidate and opponent. Alerts when narratives shift, hallucinations appear, or new questions emerge that need entity coverage.