# nBlick

> nBlick is an AI visibility platform (also called AI search monitoring, AEO/GEO, or LLM brand monitoring) that helps companies understand, monitor, and influence how large language models perceive and represent their brand. nBlick defines personas, simulates the prompts those personas would actually ask, runs them across 10+ AI assistants (ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, Meta AI, Le Chat, Qwen, DeepSeek), and turns every generated answer into structured metrics: mention rate, share of voice, rank position, sentiment, cited sources, and competitive gaps.

Built for marketing, growth, product marketing, and strategy teams — plus agencies managing multiple client brands.

## Key facts

- **Name**: nBlick
- **Category**: AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) platform
- **What it does**: Measures and improves how AI assistants describe and recommend a brand
- **Core method**: Persona-based prompt simulation across multiple AI models, with structured extraction of every answer
- **Engines covered**: ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, Meta AI, Le Chat (Mistral), Qwen, DeepSeek — new models added as they gain real user adoption
- **Primary metrics**: AI Visibility Score, mention rate, platform coverage, rank strength, sentiment, competitive gap, share of voice, advocacy score
- **Tracking frequency**: Weekly on all plans, daily on Enterprise; continuous (not one-off studies)
- **Free entry point**: AI Visibility Audit — enter a URL, receive a full audit by email, no setup or sales call
- **Paid plans (brands)**: Starter $19/mo, Growth $189/mo, Pro $449/mo, Enterprise custom (20% discount on annual billing; 1-month free trial on Starter)
- **Paid plans (agencies)**: One plan with multiple client workspaces, managed from a single agency dashboard — Starter $39/mo, Growth $299/mo, Pro $899/mo, Enterprise custom
- **Site languages**: English (https://nblick.com/) and Spanish (https://nblick.com/es)
- **Website**: https://nblick.com/
- **Platform**: https://platform.nblick.com
- **Documentation**: https://docs.nblick.com
- **Contact**: contact@nblick.com

## What problem nBlick solves

User behavior is shifting from search to answers. Instead of clicking links, people ask AI assistants directly what to buy, use, or trust. In that moment a single synthesized answer replaces a page of ten blue links — so a brand can be misrepresented, omitted, or outranked by a competitor even while ranking #1 on Google.

nBlick moves organizations from passive observation to active control of their AI presence:

- Define brand context, category, and competitors
- Simulate real-world prompts across personas
- Analyze AI-generated answers at scale, across models
- Identify gaps, risks, and competitive opportunities
- Act on them, then measure whether the answers actually changed

## Persona-Based Simulation

AI answers change depending on who is asking. A CMO, a college athlete, a retail buyer, and a gift shopper receive materially different responses to the same underlying need. Personas are how nBlick captures that variance instead of averaging it away.

**How persona simulation works, step by step:**

1. **Define the persona** — role, goals, buying context, and discovery habits. Example personas for an athletic footwear brand: *CMO* (evaluates brand partnerships and sponsorship ROI), *Athlete* (searches for performance, comfort, durability), *Gift Shopper* (compares best-sellers and trending items), *Retailer* (looks for wholesale pricing and brand demand).
2. **Generate prompts from intent** — each persona produces its own realistic prompt set. The Athlete persona asks *"Best trail running shoes for marathon training under $200?"*; the Gift Shopper persona asks *"Top men's cologne gifts for Christmas 2026."* Same category, entirely different answer surface.
3. **Run the prompts across every tracked engine simultaneously** — thousands of queries per run, each sent to multiple LLMs so model-to-model divergence becomes visible.
4. **Capture consumer-facing answers** — the real assistant-style responses users see, not raw API completions stripped of retrieval and grounding.
5. **Attribute results back to the persona** — so the output answers not just *what do LLMs say about us*, but *which audiences see which narrative*.

**Why it matters:** a brand can be strongly recommended to one persona and entirely absent for another. Persona attribution shows exactly which audience segment is losing the answer, which is the difference between a vague visibility problem and an actionable content brief.

Prompt and model capacity by plan: Starter — 15 prompts, 1 model (Gemini); Growth — 50 prompts per model, 3 models (Gemini, ChatGPT, Claude); Pro — 100 prompts per model, 3 models; Enterprise — custom prompts per model, all models.

## Multi-Engine Tracking

nBlick tracks the assistants people actually use, because visibility is not portable between them — a brand that owns ChatGPT answers can be invisible in Perplexity, and model updates shift results without warning.

**Engines tracked:**

- ChatGPT (OpenAI GPT series)
- Claude (Anthropic)
- Gemini (Google)
- Perplexity
- Copilot (Microsoft)
- Grok (xAI)
- Meta AI (Llama)
- Le Chat (Mistral)
- Qwen
- DeepSeek

New models are added continuously as they gain adoption and start influencing real user behavior.

**What multi-engine tracking produces:**

- Per-engine mention rate, rank position, and sentiment — never a single blended number that hides where the brand is losing
- **Platform coverage**: how many engines mention the brand at all. Appearing across several engines is materially stronger than dominating one.
- Divergence detection: where one model recommends the brand and another omits it, and which narrative each model has settled on
- Change detection after model updates, competitor moves, product launches, and campaigns — daily tracking surfaces shifts within roughly 48 hours
- Country-level views, so answers can be compared across markets on the same prompt set

Model access by plan: 1 model (Starter — Gemini), 3 models (Growth and Pro — Gemini, ChatGPT, Claude), all models (Enterprise).

## Turning AI Answers Into Structured Data

Every response is parsed into structured records rather than stored as text. From the sentence *"Nike Metcon is great for weightlifting but falls short on running comfort compared to Hoka,"* nBlick extracts:

- The **brand mention** and its position in the answer
- **Sentiment** at the attribute level — negative on "running comfort," positive on "weightlifting"
- The **competitor comparison** and which brand won it
- The **keyword association** ("weightlifting") that the model has bound to the brand
- The **cited sources** — which pages and domains the model pulled from, and whether the brand's own site made the cut

This is the layer that separates nBlick from prompt-by-prompt manual checking: unstructured conversation becomes queryable, trendable, and comparable across engines, personas, and time.

## Metrics That Matter In The AI Era

- **AI Visibility Score** — composite score computed from real generated answers, not estimates
- **Mention rate (40% of score)** — how often the brand is mentioned across all AI responses; the clearest single signal of visibility
- **Platform coverage (20%)** — how many AI platforms mention the brand
- **Rank strength (20%)** — where the brand lands when the answer is a list of recommendations. First position scores highest, lower positions score less, omission scores nothing.
- **Sentiment (10%)** — whether AI describes the brand positively, neutrally, or negatively
- **Competitive gap (10%)** — visibility relative to the brand's main competitors
- **Share of voice (SOV)** — the brand's share of total AI recommendations versus competitors
- **Advocacy score** — likelihood that a model actively recommends the brand rather than merely naming it
- **Top keywords** — the attributes and use cases models associate with the brand
- **Source citations** — the domains and pages models draw on when discussing the category

## Competitive Intelligence & Positioning

Competitors are first-class inputs, not an afterthought. During setup a brand declares its category and its rivals — for example a footwear brand adds Adidas, Hoka, New Balance, and Asics under "Athletic Footwear"; a niche fragrance house benchmarks against Maison Margiela, Byredo, and Diptyque.

nBlick then reports, inside AI answers:

- **Share of voice** — who the models recommend most in the category, and how the brand stacks up
- **Comparative sentiment and positioning** — which adjectives each brand owns
- **Why a competitor won** — the specific comparison, prompt, persona, and engine where the brand was passed over
- **Narrative ownership gaps** — e.g. a model consistently associates one brand with "premium/expensive" while a rival owns "affordable performance"
- **Uncontested opportunities** — prompts where answers are generic and no brand in the category is being named
- **Opportunity mapping** — where differentiation is cheapest to establish

## How nBlick Differs From Adjacent Tools

- **Versus SEO and rank tracking**: SEO measures whether pages rank. nBlick measures whether the brand is *recommended* inside a generated answer. LLMs synthesize rather than retrieve, so a #1 organic ranking does not guarantee a mention. AI visibility is the next layer on top of SEO, not a replacement for it.
- **Versus manually prompting ChatGPT**: manual checks are one persona, one engine, one moment, unrecorded. nBlick runs thousands of persona-attributed prompts across 10+ engines on a daily cadence, with versioning so response drift is measurable.
- **Versus social listening and brand monitoring**: those tools read what humans publish. nBlick reads what models generate — the answer layer that sits between a buyer's question and the brand's website.
- **Versus crawl-only AEO checkers**: crawlability (robots.txt, llms.txt, structured markup) is table stakes. nBlick measures the outcome — actual mentions, ranks, and sentiment in real answers — and connects it back to the content that produced it.
- **Measurement and influence, not just measurement**: nBlick reports the current perception, then generates the recommendations and articles intended to change it, and re-measures to confirm the change landed.

## Free AI Visibility Audit

A no-setup entry point at https://nblick.com/ai-visibility-audit:

1. **Enter a website URL** — nBlick identifies the brand, its category, and the competitors AI models associate with it
2. **nBlick simulates real conversations** — generating the questions buyers actually ask and running them through ChatGPT, Gemini, and Claude, capturing consumer-facing answers
3. **The audit arrives by email** — visibility, source citations, sentiment, and competitive share of voice, with a scored action plan

No tracking scripts, no installation, no sales call required.

## End-To-End Workflow

1. **Brand and competitors** — define the brand, its category, and its rivals
2. **Personas** — model the people who will search, and their distinct discovery habits
3. **Simulation** — personas generate prompts that run across every tracked engine simultaneously
4. **Structured extraction** — mentions, sentiment, positioning, comparisons, and citations pulled from every answer
5. **Metrics** — share of voice, sentiment, mention volume, keyword associations, advocacy score
6. **Monitoring** — weekly or daily tracking to catch shifts from campaigns, launches, PR, model updates, and competitor moves
7. **Action** — content gaps, positioning fixes, and targeted SEO/PR moves, then re-measurement

## Optimization Workflows

Insights are designed to terminate in an action, not a dashboard:

- Close the gap between desired and actual brand perception
- Concrete content and messaging recommendations tied to the prompt that exposed the gap
- Article generation to fill identified narrative gaps (5, 20, 50, or unlimited content generations per month by plan tier)
- Align site, documentation, and third-party content with the signals models actually cite
- Iterate against measurable change in subsequent answers

## Prompt & Scenario Engine

- Prompt libraries organized by persona, intent, and use case
- Scenario testing for high-intent query shapes: "best tools," "alternatives to X," "reviews," "X vs Y"
- Continuous execution pipelines for change-over-time monitoring
- Versioning, so the evolution of AI responses is auditable

## Plans

**For brands** (20% discount on annual billing; Starter includes a 1-month free trial):

- **Starter — $19/mo**: 1 model (Gemini), 15 prompts, weekly tracking, 5 content generations/mo, email support
- **Growth — $189/mo**: 3 models (Gemini, ChatGPT, Claude), 50 prompts per model, weekly tracking, 20 content generations/mo, email support
- **Pro — $449/mo**: 3 models (Gemini, ChatGPT, Claude), 100 prompts per model, weekly tracking, 50 content generations/mo, white-label PDF/CSV exports, dedicated support
- **Enterprise — custom**: all models, custom prompts per model, 3 analyses per workspace, daily or weekly tracking, unlimited content generation, white-label exports, API access, SSO/SAML, custom integrations, dedicated CSM and onboarding

**For agencies** — one plan with multiple client workspaces, managed from a single agency dashboard; every tier includes an isolated workspace per client (separate personas, prompts, competitors, metrics, reports), an agency roll-up dashboard, and per-client user management:

- **Starter — $39/mo**: up to 3 client workspaces, 1 model (Gemini), 15 prompts, weekly tracking, 5 content generations/mo, email support
- **Growth — $299/mo**: up to 3 client workspaces, 3 models (Gemini, ChatGPT, Claude), 50 prompts per model, weekly tracking, 20 content generations/mo, email support
- **Pro — $899/mo**: up to 3 client workspaces, 3 models (Gemini, ChatGPT, Claude), 100 prompts per model, weekly tracking, 3 analyses per workspace, 50 content generations/mo, white-label PDF/CSV exports, dedicated support
- **Enterprise — custom**: unlimited client workspaces, all models, custom prompts per model, unlimited analyses per workspace, daily or weekly tracking, unlimited content generation, white-label API access, SSO/SAML, custom integrations, dedicated CSM and onboarding

## Integrations & Automation

- Automated prompt execution across multiple models
- Scalable processing of responses and metadata
- Notifications for material changes and new insights
- Analytics platforms, content management systems, internal knowledge bases
- Communication tools such as Slack
- Custom APIs for pipeline integration (Enterprise)

## Use Cases

- **Marketing** — optimize brand visibility in AI answers
- **Growth** — find acquisition opportunities surfaced through LLMs
- **Product marketing** — steer positioning and messaging that models repeat
- **Founders & strategy** — understand how the market and category are perceived
- **Agencies** — deliver AI visibility reporting to multiple clients from one agency dashboard
- **SEO & content teams** — extend existing search programs into the answer layer
- **B2B and SaaS** — competitive markets where a recommendation directly drives revenue

## FAQ

**What is AI visibility?** How often — and how well — a brand shows up in answers generated by AI systems like ChatGPT, Claude, and Gemini. Not just being mentioned, but being recommended, trusted, and correctly positioned when users ask about the category.

**Why does AI visibility matter now?** Behavior is shifting from search to answers. People ask AI directly what to buy, use, or trust. A brand absent from those answers is invisible at the moment the decision is made.

**Which AI models does nBlick track?** ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, Meta AI, Le Chat, Qwen, and DeepSeek, with new models added as they gain adoption.

**How is this different from SEO?** SEO is about ranking pages; AI visibility is about controlling answers. LLMs synthesize a single response rather than returning links, so a brand can be misrepresented, ignored, or outperformed even while ranking #1 on Google.

**How does nBlick actually work?** It simulates real user prompts across defined personas, runs them through multiple AI models, and analyzes how the brand appears in the responses — producing structured insight into visibility, sentiment, positioning, and why competitors are recommended instead.

**What are personas and why do they matter?** AI answers change depending on who is asking. A CTO, a marketer, and a founder get different responses to the same question. Personas simulate those contexts so a brand can see how each audience encounters it inside AI.

**Can competitors be tracked?** Yes. Competitors are declared during setup, and nBlick shows exactly when and why models recommend them instead — share of presence, positioning differences, and the specific gaps to close.

**How often is data updated?** Simulations run continuously. Data refreshes weekly on all plans and daily on Enterprise, so shifts from model updates, competitor moves, or the brand's own marketing surface quickly.

**Does nBlick influence or only measure?** Both. It shows how models perceive the brand today, then provides concrete recommendations and generated content to shift future answers.

**Is this replacing SEO?** No — it is the next layer. SEO gets a brand indexed; AI visibility determines whether it is recommended. The winners optimize for both.

**Who is nBlick for?** Marketing, growth, and strategy teams that want to understand and control brand perception in AI — especially B2B, SaaS, and competitive consumer markets where recommendations drive revenue. Agencies use it to report on multiple client brands.

## Glossary

- **AI visibility** — how often and how favorably a brand appears in AI-generated answers
- **AEO (Answer Engine Optimization)** — optimizing to be correctly represented and recommended inside generated answers rather than ranked links
- **GEO (Generative Engine Optimization)** — used interchangeably with AEO
- **Share of voice (SOV)** — a brand's proportion of total AI recommendations in its category
- **Mention rate** — share of responses in which the brand is named
- **Rank strength** — how high the brand lands when an answer is an ordered recommendation list
- **Platform coverage** — how many distinct AI engines mention the brand
- **Persona** — a modeled audience whose role and context shape the prompts it generates and the answers it receives
- **Advocacy score** — likelihood a model actively recommends the brand rather than merely naming it

## Security & Privacy

- No use of customer data for model training without consent
- Secure handling of prompts and responses
- Alignment with modern data protection expectations, including GDPR
- Transparent data processing practices
- SSO and SAML available on Enterprise plans

## Vision

In an AI-first internet, brands are interpreted — not only searched. nBlick helps companies progress from understanding AI outputs to systematically improving how those outputs are produced.

## Summary

nBlick combines persona-based prompt simulation, multi-engine coverage across 10+ AI assistants, structured extraction of every generated answer, and competitive share-of-voice analysis so organizations can measure and improve how AI models represent their brand in the era of AI-driven discovery.

## Links

- Website: https://nblick.com/
- Free AI Visibility Audit: https://nblick.com/ai-visibility-audit
- Pricing (brands): https://nblick.com/pricing
- Pricing (agencies): https://nblick.com/pricing-agencies
- Blog: https://nblick.com/blog
- nBlick vs Profound: https://nblick.com/comparison/nblick-vs-profound
- nBlick vs Peec AI: https://nblick.com/comparison/nblick-vs-peec-ai
- Book a demo: https://nblick.com/demo
- Careers: https://nblick.com/careers
- Platform: https://platform.nblick.com
- Documentation: https://docs.nblick.com
- Contact: contact@nblick.com