AI Visibility Platform
AI View Sync
See how AI models talk about your brand — then fix it, automatically.
01
Problem
Buyers increasingly ask AI assistants — not Google — what product to use. Most companies have zero visibility into whether ChatGPT, Claude, Gemini, or Perplexity recommend them, mention them accurately, or recommend a competitor instead.
SEO tooling measures rankings on a results page that fewer buyers see every quarter. There was no equivalent of Search Console for the AI layer: no audit, no share-of-voice metric, no way to know if your content is even legible to a language model.
Even teams that understood the problem had no path from insight to action. Knowing an LLM misdescribes your pricing is useless if fixing it means a quarter-long content project.
02
Solution
AI View Sync runs structured audits against multiple frontier models, asking the questions real buyers ask and scoring how each model represents the brand — accuracy, sentiment, recommendation rate, and share of voice against named competitors.
It then closes the loop: the platform generates llms.txt files, FAQ schema, and content recommendations mapped to the gaps it found, and syncs approved changes directly to WordPress. The audit isn't a report — it's a queue of fixes with an apply button.
A growth roadmap ties it together, sequencing the highest-leverage fixes first so a marketing team can improve AI visibility the way they'd run a CRO program: measure, change, re-measure.
Capabilities
What it does
AI Recommendation Audit
Structured prompt batteries simulate real buyer questions across models and score whether — and how — the brand gets recommended.
LLM Visibility Analysis
Measures how accurately each model describes products, pricing, and positioning, and flags hallucinated or stale claims.
Competitor Analysis & Share of Voice
Tracks recommendation rate against named competitors over time — the AI-era equivalent of rank tracking.
AI Traffic Reporting
Segments referral and assistant-driven traffic in GA4 so teams can see what AI visibility is actually worth.
llms.txt & FAQ Schema Generation
Generates machine-readable brand context and structured data so models cite the source of truth instead of guessing.
WordPress Sync & Automated Implementation
Approved recommendations publish directly to the site — schema, content updates, and llms.txt — without a developer in the loop.
Content Recommendations
Maps every visibility gap to a concrete content change, prioritized by expected impact on recommendation rate.
Growth Roadmap
Sequences fixes into a measurable program: baseline audit, implementation sprints, and scheduled re-audits.
Architecture
How it's put together
The platform is a Next.js app over a job-queue backend. Audits fan out across model APIs in parallel, results are normalized into a scoring schema, and an implementation engine turns findings into deployable artifacts.
Client
Next.js Dashboard
Audits, SoV, roadmap
Reports
Scheduled PDF / email
API & Orchestration
REST API
Auth, orgs, projects
Audit Queue
Fan-out job runner
Scheduler
Re-audit cadence
AI Layer
Prompt Battery Engine
Buyer-intent question sets
Claude · GPT · Gemini · Perplexity
Parallel model calls
Scoring & Extraction
Accuracy, sentiment, SoV
Implementation
llms.txt Generator
Schema Builder
FAQ / Organization JSON-LD
WordPress Sync
REST API publisher
Data
Postgres
Audits, scores, history
GA4 / BigQuery
AI traffic reporting
Challenges
What was hard, and what I did about it
LLM answers aren't deterministic
The same question yields different answers run to run. Single-shot audits were noise. The fix: repeated sampling per question, answer clustering, and confidence intervals on every score — treating model output as a distribution, not a fact.
Scoring 'how a model talks about a brand'
Recommendation rate is easy; accuracy and sentiment are not. I built an extraction layer where a second model grades answers against a structured brand fact sheet, with spot-check tooling to keep the grader honest.
Automated publishing people can trust
Nobody lets a tool write to their production site on day one. Every change ships through a review queue with diffs and one-click rollback, which turned 'automated implementation' from scary to adopted.
Tech stack
Built with
Outcomes
What changed
- 4 modelsaudited in parallel per brand, with per-model share-of-voice tracking
- Minutesfrom audit finding to published fix via WordPress sync — previously a content-team quarter
- Repeatableaudit → implement → re-audit loop that turns AI visibility into a measurable channel
Future roadmap
Where it goes next
Agentic audits: models that browse a site the way an assistant's retrieval layer does, not just answer questions
Shopify and headless CMS sync targets alongside WordPress
Alerting when a competitor's recommendation rate moves against you
Public AI visibility benchmarks by industry
Next project
Heynet