How AI Native works, end to end.

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• Create your workspace - Sign up, verify your email, and land in your new AI Native workspace.
• Add your first brand - Use the onboarding wizard, choose demo or live discovery, then review before launch.
• Run your first scan - What a scan does, the difference between demo and live, and how credits work.
• Brand profile settings - What lives in the five settings tabs, how per-profile sharing works, who pays for scans, and how to manage your team, billing, CMS connector, and activity log.
• Demo discovery vs live discovery - Understand the two onboarding modes, when each makes sense, and what changes when you switch from demo to live.
• Finalise your brand identity (names + domains) - How the brand identity registry works, why it matters for measurement, and how to add name variations and domains after onboarding.
• Read your first results - How to interpret the numbers on your first scan, what recommendation rate and mention rate mean, and what to check first.
• Invite your team and set roles - How to invite teammates to a brand profile, understand the four roles, and how per-profile sharing works.
• The command center - The workspace home screen: where you stand in AI answers and what to do this week.
• The Brand Truth Studio - Verify facts, manage your brand identity registry, and set the guardrails answers are checked against.
• Scans and results - The run page, reading mention versus recommendation, and the sources behind each answer.
• "Scans: demo mode vs live mode" - What separates a demo scan from a live scan, what each costs, and when to use which.
• Product overview pages - A tour of the product overview screen, the KPI tiles, the outcome ladder, the two-surface split, and how to navigate to deeper views.
• "The prompt library: questions, tags, versions" - How prompts are structured, what the tree view shows, how to add or import questions, and what happens to your data when you edit the set.
• The answers explorer - Read the raw AI answers behind your scan numbers, filter by outcome and engine, and trace inaccurate claims back to the fact that was broken.
• Competitor discovery and tracking - How AI Native discovers competitors from your own scan data, what the share-of-voice leaderboard shows, and how to track and tier the names that matter.
• Personas and buyer journeys - How need-state personas work, what the 6-stage journey profile contains, how business weight affects scoring, and how to add or edit personas.
• "The WHY ledger: sources behind every gap" - How the WHY ledger turns scored answers into evidence-grounded reasons for each gap, with a topic and a channel to act on.
• The brand identity registry - How the identity registry controls presence measurement, what each field contains, how to manage it, and how to re-apply changes to historical scan data.
• "The action plan: attack and defend queues" - How opportunities are scored and ranked, what the attack and defend queues contain, and how to track what you shipped and measure whether it worked.
• "Billing, credits, and the transparent ledger" - How credits work, how estimates are formed, what the ledger shows for every transaction, and what happens to your balance when a scan succeeds, refunds, or fails.
• Brand guardrails - How to define rules for how AI should represent your brand, what the four rule types check, and how to read the compliance score.
• Content Studio and CMS publishing - How to generate content drafts from measurement gaps, move them through draft to published, and send them to your connected CMS.
• "Experiments: before and after" - How to freeze a baseline for one query, record one action, and let the verdict compute itself from later scans.
• Reports and share links - How to access the executive report, create tokenized read-only share links for clients, export raw data as CSV, and revoke links.
• Trends and history - How to read the trend chart, what the range and granularity filters do, how series breaks are marked, and what the action pins mean.
• AI share of voice - What share of voice measures in AI answers, and how to read it against mention and recommendation.
• Mention versus recommendation - The difference between being named in an AI answer and being put forward as a choice.
• The visibility ladder - The five-rung outcome scale that every scan result sits on, and what each rung means for your brand.
• Sources and citations - The difference between grounded answers that cite sources and parametric answers that draw on model memory, and why it matters for your AI visibility.
• Accuracy and fact checks - How AI Native checks whether AI answers get your product facts right, what gates the audit, and what the accuracy states mean.
• Why AI numbers vary between scans - AI engines are stochastic, and the same question can produce different answers across runs. Here is why your numbers move, and how to read the signal from the noise.
• Demo data versus live data - What demo mode is for, how it differs from live data, why the two never mix, and when to switch to live.
• Sentiment in AI answers - What sentiment measures in AI answers, how in-AI framing and web-source polarity are separated, and what to do when the number is negative.
• Demand and value scoring - How AI Native grounds each prompt in real search demand and combines it with intent and funnel position to produce a prompt value score.
• The opportunity score - How AI Native combines value, visibility gap, and winnability into a single ranked number that tells you which cell to address first.
• What is AEO and GEO? - Answer-engine and generative-engine optimisation, defined plainly, with an honest note on the unsettled terms.
• How AI engines build answers - The two-stage process AI engines use to form an answer, why it matters for your brand, and where each stage creates a lever you can pull.
• How to get cited by AI engines - The practical content and structural moves that make AI engines more likely to cite your pages and name your brand in answers.
• Schema and structured data for AI - How structured data on your pages helps AI engines extract precise facts, and which schema types matter most for AI visibility.
• The llms.txt file - What the llms.txt convention is, what it does and does not do, and how to decide whether publishing one is worth the effort for your brand.
• AEO for regulated industries - How to make financial, health, and legal content citable by AI while meeting disclosure requirements - the two goals are compatible when the approach is precise.
• AI search in India - How AI answer engines are being used in India, what is distinct about the Indian market for AI visibility, and the practical actions that matter most here.
• AEO and SEO: complements, not rivals - How classic search optimisation and AI visibility work relate, where they diverge, and how to manage both without doubling your workload.
• Comparison pages and listicles - How to earn presence in third-party comparison guides and build honest first-party comparison pages that AI engines actually cite.
• Reddit, Quora, and review sites - How earned presence on user-generated content platforms shapes AI answers, and the legitimate participation principles that govern this work.
• Wikipedia, Wikidata, and entity presence - How structured entity records on Wikipedia and Wikidata shape an AI engine's understanding of your brand, and what you can actually do about it.
• Measuring AEO success - The metrics that tell you whether AI visibility work is moving, how to read mention rate and recommendation rate separately, and how to run before-and-after measurement that proves a change worked.
• Common AEO mistakes - The patterns that consistently produce no result or negative results in AI visibility work, and what to do instead.
• Freshness and update cadence - How content age affects AI visibility, which content types decay fastest, and how to set a realistic maintenance cadence for your highest-value pages.
• Common questions - Straight answers on engines, scanning cadence, demo versus live, credits, data, and results.
• Scans and credits - How scan credits work, what you are charged for, how estimates and final bills relate, and what happens if something goes wrong.
• Data handling and security - What data AI Native processes, what leaves your workspace, how workspaces are isolated, and what structural protections are in place.
• Metrics and numbers - Straight answers on what the numbers in a scan result mean, how they are computed, and how to read them correctly.
• Engine coverage - Which AI answer surfaces AI Native measures, how each works differently, and what that means for your scan results.
• Accounts, brand profiles, and roles - How brand profiles, memberships, and the four roles work, including per-profile sharing, who pays for scans, and how to invite team members.

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