AI Visibility Platform for ChatGPT, Gemini, and answer engines
Measure your AI visibility, benchmark competitors across key prompts, identify visibility gaps, grow Answer Share, and improve how often your brand is recommended or cited in AI-generated answers.
Track AI visibility across prompts and models, not just pages or keywords.
- •AI Visibility Score™ across major AI systems (GPT, Gemini)
- •Competitive benchmarking across tracked prompts
- •Prompt coverage and visibility gaps by topic and intent
- •Built-in optimization through Signals, FAQ, and structured publishing
Predict how AI visibility will change before you publish.
From measurement → to gaps → to fixes → to improved AI visibility
Why AEO and GEO Matter for AI Visibility
Answer Engine Optimization (AEO) focuses on how often your brand is included, cited, or recommended in AI-generated answers across systems like ChatGPT and Gemini.
Generative Engine Optimization (GEO) focuses on how your content is structured, published, and reinforced so AI systems can interpret, retrieve, and reuse it in those answers.
Together, AEO and GEO define how brands are selected, trusted, and recommended in AI systems.
- •AEO measures your presence in AI-generated answers
- •GEO improves how your content is understood and retrieved
- •AI visibility combines both into a measurable, improvable system with Answer Share you can track across prompts and models
AI Visibility Score™
Measure how often your brand is recommended or cited in AI-generated answers across major AI systems. Track score, visibility trends, and momentum over time. This is performance in AI answers, not publishing volume. Continuous tracking ensures visibility improvements compound over time, not just snapshot measurements.
- •0–100 score with visibility trends and momentum, not a one-time snapshot
- •Cross-model coverage (GPT, Gemini) and consensus tracking
- •Week-over-week delta so you see acceleration or drift in visibility across prompts and models
- •Authority stage framing (for example Emerging Authority) with plain-language context
- •Guidance that ties score, coverage, and recommendations to the next optimization moves
Track visibility trends and momentum, not only a static score or publishing cadence
Competitive Benchmarking
BetaCompare your AI visibility against competitors across high-intent prompts and answer engines. See which brands are recommended, which are gaining Answer Share, and where you are missing. Predictive AI visibility: estimate how changes to your content and signals will impact AI visibility before you publish.
- •Side-by-side prompt coverage across competing brands
- •Answer Share and visibility comparison where supported
- •Identify prompts where competitors are recommended and you are not
- •Leaderboard views across brands for each prompt cluster
- •Estimate how new signals will affect Answer Share and prompt coverage
- •Simulate visibility improvements across key prompts and models
- •Validate what to publish before investing in content
Benchmark competitors on the prompts that drive discovery and selection
Prompt Coverage & Visibility Gaps
Track your presence across discovery, comparison, and buying prompts in AI-generated answers. Group gaps by topic, use case, or intent so you know what to fix next and can prioritize with confidence.
- •Coverage across a continuously expanding set of high-intent prompts per category
- •Track visibility across dozens of high-impact prompts per category, refined over time
- •Missing prompts where your brand is not recommended or cited in AI-generated answers
- •Gap grouping by topic, use case, and intent for faster prioritization
- •Prioritized actions to improve visibility on high-impact prompts
See prompt-level coverage and gaps that block recommendations in AI-generated answers
AI Visibility Diagnostics
Understand exactly why AI systems are not recommending your brand, how that shows up in your visibility score and prompt coverage, and what to fix next.
- •Weak entity clarity, missing evidence, or insufficient authority signals
- •Gaps that reduce recommendation confidence across AI systems
- •Prioritized issues that limit your visibility score and coverage
- •Clear path from diagnosis to on-domain optimization and stronger recommendations
- •Understand which prompts and categories you are losing and why
See why models hesitate before you add more pages
Built-in Optimization: Signals, FAQ, and Structured Publishing
Turn visibility insights into on-domain assets that improve AI recommendations. Publish structured signals, generate FAQ and answer-ready pages, and reinforce comparisons and category relevance in AI-generated answers.
- •Structured signals on your domain for models and crawlers
- •FAQ and answer-ready pages generated and refreshed on a steady cadence
- •Use-case and comparison signals that increase recommendation likelihood
- •Route visibility gaps into structured signals that strengthen future recommendations
From gaps to signals to improved recommendations on your domain
LLM Crawl Readiness & On-Domain Infrastructure
Ensure your content is discoverable, interpretable, and reusable by AI systems through structured, crawlable, on-domain infrastructure.
- •llms.txt support with validation workflows you can run on repeat
- •Machine-readable publishing that reinforces entity clarity and recommendation signals
- •Structured signals designed to be retrieved and cited in AI-generated answers
- •Structured on-domain authority instead of disconnected pages
- •Crawlable hubs that stay discoverable as models refresh
Infrastructure that supports AI visibility, retrieval, and citation
Measure and improve AI visibility, not just monitor it
FreshNews.ai is an AI visibility platform that combines measurement, benchmarking, diagnostics, and optimization in one system.
FreshNews.ai is purpose-built for the AI-first search landscape, not adapted from traditional SEO tools.
Most tools stop at measurement. FreshNews.ai connects measurement, diagnostics, and optimization in one system.
| Feature | FreshNews.ai | Traditional AI Visibility Tools |
|---|---|---|
| AI Visibility Score | 0–100 score with weekly trends, momentum, and cross-model context in one program | Static scores or mention counts without a continuous improvement loop |
| Prompt Coverage | Tracked prompts with visibility across discovery, comparison, and buying intent | Shallow keyword lists or incomplete prompt libraries |
| Competitive Benchmarking | Compare brands on shared high-intent prompts; optimization in the same workspace (Beta on selected plans)Beta | Ad hoc research or siloed enterprise add-ons |
| Answer Share Tracking | Answer Share and competitive visibility where engines support comparison | Limited or no Answer Share tied to prompts and models |
| Visibility Gap Analysis | Prompt-level gaps grouped by topic, use case, and intent | Generic alerts with weak prioritization |
| Recommendation Diagnostics | Why recommendations fail: entity clarity, evidence, authority, structured signals | Dashboards that still need heavy manual interpretation |
| Built-in Optimization Layer | Signals, FAQ, and structured publishing alongside measurement for a closed loop | Measurement only; no native optimization or on-domain authority system |
| Structured On-Domain Signals | Purpose-built assets on your domain that models can retrieve and cite | PDFs, ad hoc blogs, or pages not built for AI retrieval |
| LLM Crawl Readiness (llms.txt, schema) | llms.txt automation, validation, and structured data aligned to AI visibility | Manual llms.txt or no coordinated crawl readiness for AI systems |
| Multi-model Tracking (GPT, Gemini) | Visibility across major models and answer engines in one program | Often single-surface or single-source monitoring |