Clear answers for search, AI and your team

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SAG finds where a service is missing, unclear or weakly supported when customers search, compare options or ask an AI.

Your team receives a practical backlog of copy, FAQ, page structure and evidence improvements instead of a score without context.

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SAG is designed for marketing, product, content, growth and digital strategy teams that need a shared view of search and AI visibility.

It is especially useful when customers repeat the same questions or competitors are described more clearly.

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SAG can be configured for commerce, finance, travel, software, professional services and other industries where customers compare information before choosing.

The final question set, evidence rules and review scope are adapted to the service and market.

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Yes.

SAG can organize B2B buying questions such as integration, security, pricing, implementation, proof and support into answer-ready content. These questions are linked to approved product and company evidence before recommendations are published.

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SAG helps commerce teams explain product differences, suitability, conditions, delivery, returns and proof in language customers can compare.

It also identifies missing answers that may prevent products from being understood in search and AI responses.

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SAG separates product conditions, comparison criteria, risks and verifiable sources so financial information is easier to understand.

It does not promise returns or recommendations, and regulated claims remain subject to the customer’s legal and compliance review.

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SAG maps questions about destination, eligibility, availability, restrictions and service fit to the relevant pages and evidence.

The result is clearer content for customers choosing between similar offers.

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SAG can complement an agency or internal team by providing a shared evidence model, question coverage analysis and prioritized actions.

Implementation ownership is agreed separately, so existing teams can keep their publishing and governance process.

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A successful engagement produces clearer answers, documented evidence, accountable actions and comparable rechecks.

Search ranking, AI citation and revenue are observed outcomes rather than guaranteed deliverables.

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SAG reviews page purpose, titles, headings, internal links, crawl and index signals, and the relationship between service claims and supporting pages.

The exact technical checks depend on the approved domain and access available.

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SAG checks whether important customer questions receive a direct, complete and well-structured answer.

It also reviews definitions, conditions, evidence, follow-up actions and consistency across related pages.

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SAG checks whether a brand is described consistently and supported by sources that AI systems can interpret and reference.

GEO work includes entity clarity, answer coverage, evidence gaps and observed AI responses under recorded conditions.

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SAG can evaluate agreed services such as ChatGPT, Gemini, Perplexity, Microsoft Copilot, Claude and other accessible AI channels.

Availability, collection method, language, market, question set and frequency are recorded for every check.

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The scope can include Google, NAVER, Bing, Yahoo!

JAPAN, Baidu, Yandex and DuckDuckGo where access and market relevance allow. Each project defines which engines matter instead of treating every channel as equally important.

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Not always.

Consumer AI apps can vary by model, search connection, personalization, location and interface, so SAG records the service, question, language, market, time and repetition count used for an observation.

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No.

SAG improves answer quality, entity clarity and evidence accessibility, but an external AI system decides what it cites or recommends. Reports separate controllable improvements from observed third-party outcomes.

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No.

SAG provides technical and content improvements that support discoverability, but search engines control ranking. Changes are measured after implementation without presenting a target as an achieved result.

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Clear entities, direct answers, stable terminology, descriptive headings, internal links and verifiable sources make content easier to interpret.

SAG identifies where these elements are missing or contradictory.

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Structured data helps machines understand the type and relationships of visible content when it accurately matches the page.

It supports interpretation but does not guarantee a rich result, ranking, citation or recommendation.

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A strong FAQ expresses real customer questions in natural language and answers them directly before adding detail.

It also creates stable question-and-answer units that search and AI systems can parse, provided the content is accurate and visible to users.

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Useful evidence can include official product pages, policies, pricing, documentation, certifications, case studies and approved company information.

SAG records the source and review status so unsupported claims are not treated as facts.

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Yes.

The target countries, languages, search engines, AI channels and approved source material are defined during setup. Translations should be reviewed for local meaning rather than copied mechanically from another market.

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Consistent names, descriptions, capabilities and evidence reduce ambiguity about what a brand offers.

SAG identifies conflicting statements across pages so teams can establish one approved explanation.

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The baseline records the approved domains, customer questions, competitors, languages, markets, channels and observation period before changes are applied.

Unavailable data is marked unmeasured rather than converted into a zero.

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Customer questions come from sales, support, search demand, product research and stakeholder interviews, then are reviewed with a SAG expert.

The final set reflects real decision points instead of a generic keyword list.

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SAG records the question, channel or model, language, market, time, repetition count, target page and relevant configuration.

These conditions make later comparisons more meaningful.

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SAG marks the item as unsupported, unavailable or unmeasured and records the reason.

It is never reported as no problem, a zero score or a successful result.

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SAG reuses comparable questions and conditions after approved improvements are published.

The report shows what changed in the content, what changed in observed answers and what still requires evidence or another review.

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Depending on scope, SAG can track question coverage, brand mentions, source citations, answer completeness, page readiness and observed changes by channel.

Traffic and inquiry metrics can be reviewed when the customer provides an approved data source.

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Competitors are reviewed against the same customer questions, page scope, language, market and time window.

SAG keeps the comparison criteria and sources visible so a score can be traced back to evidence.

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Repeated observations and recorded conditions help separate a one-off response from a recurring pattern.

SAG does not present one prompt or one screenshot as a universal result.

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Yes.

SAG reports connect the question, observation conditions, source evidence, recommendation, reviewer decision and revision. This provenance helps teams review and repeat the work.

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Start by sharing your service, domain, priority customer questions and the business outcome you want to improve.

A SAG expert then confirms the scope, competitors, channels, languages, access and review process.

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Prepare the website address, service description, main products or services, common customer questions, candidate competitors and a responsible contact.

Only share materials and access your organization is authorized to provide.

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A Goal combines the service, audience, countries, languages, customer questions, competitors, channels, measurement period and success criteria for one review.

It becomes the shared reference for analysis and rechecks.

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You and a SAG expert choose organizations that address the same customer problem or buying decision.

Competitors are never inserted randomly to fill an empty comparison.

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Your website is the source SAG evaluates; your SAG workspace is the private area where your team reviews Goals, evidence, reports and actions.

The approved domain and permitted page scope are confirmed before operational work begins.

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Yes.

An expert helps define the Goal, competitor set, question set, target markets and observation method before recurring checks begin. The number and duration of setup sessions are agreed before service starts.

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A typical subscription setup takes about three to five business days after materials, access and scope are ready.

A first comparison and improvement report is commonly planned one to two weeks later, subject to review and approval timing.

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The customer verifies product facts, permissions, competitor relevance, claims, legal conditions and the people responsible for implementation.

SAG records assumptions and does not publish an automated suggestion as a confirmed fact.

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Yes, if the remaining scope is useful and the limitation is documented.

Unavailable pages, channels or data sources stay marked as unmeasured and may reduce the conclusions that can be drawn.

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Yes.

SAG is designed to review and improve existing service pages, FAQs, documentation and information architecture. A rebuild is only proposed when the evidence shows that smaller content and structure changes are insufficient.

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A page-level finding identifies the customer question, affected page, observed issue, supporting evidence, proposed change, priority and recheck condition.

This makes the recommendation actionable for content, design and development teams.

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An FAQ draft contains a natural customer question, a direct first-sentence answer, supporting conditions or evidence and a relevant next action.

The customer reviews facts and regulated claims before publication.

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A SAG content brief turns observed gaps into a page objective, customer questions, required evidence, message hierarchy and acceptance criteria.

It helps writers, designers and developers work from the same decision record.

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The comparison shows how selected competitors answer the same questions, which evidence they expose and where your service is clearer or incomplete.

Sources and conditions remain attached to the comparison.

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Priority reflects customer importance, evidence gaps, page reach, implementation effort and the risk of misleading or incomplete information.

The expert review can adjust priorities to match business constraints.

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SAG provides analysis, prioritized guidance, expert review and rechecks within the standard scope.

Your team can implement the changes, or implementation support can be quoted separately with explicit ownership and approval.

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A SAG expert checks the evidence, assumptions, proposed answer, priority and implementation owner before approving a report revision.

Items without sufficient evidence remain drafts or unmeasured.

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SAG rechecks the approved questions and conditions, records the new observations and compares them with the baseline.

The next report separates content changes from external search or AI outcomes.

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Approved report revisions can be downloaded in the supported workspace formats.

Drafts, unapproved changes and customer data remain subject to workspace permissions and retention rules.

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Subscription SAG provides a managed workspace, expert setup and recurring checks; on-premises SAG is installed within the customer’s infrastructure under an agreed integration and support scope.

Data, security and operational responsibilities differ between the two.

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Starter is proposed for one site and up to five competitors, with 30 customer questions, two AI services, one language, weekly checks and a monthly report.

Final limits and methods are confirmed in the contract.

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Growth is proposed for one site and up to five competitors, with 50 customer questions, three AI services, one language, daily AI answer checks, weekly page checks and monthly improvement work.

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Pro is proposed for one site and up to five competitors, with 100 customer questions, three AI services, two languages, daily AI answer checks and one expert review each month.

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The current proposals are KRW 290,000 for Starter, KRW 590,000 for Growth and KRW 1,490,000 for Pro per month, excluding VAT.

Final scope, limits and optional implementation work are confirmed before contracting.

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Additional volume is reviewed against collection cost, expert review capacity and the required reporting cadence.

SAG proposes an adjusted scope instead of silently exceeding the agreed limit.

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Payment schedule, renewal, cancellation and refund terms are confirmed in the proposal and contract before service starts.

The public experience does not collect card or bank details, and cancellation and refunds remain separate commercial actions.

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Tenant identifiers are applied to customer-owned records and server-side authorization checks the requesting membership before access.

Shared public entities and tenant-private data follow separate storage rules.

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Workspace access is role-based so viewing, editing, approving and administrative actions can be separated.

The final role model and identity provider depend on the subscription or on-premises agreement.

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Retention periods, export needs and deletion responsibilities are defined during contracting and security review.

Requests are processed against the customer workspace and recorded so data is not removed from the wrong tenant.

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On-premises delivery can include environment review, scope definition, installation, connection, testing, training and handover.

Basic installation starts at KRW 30,000,000 and annual maintenance at KRW 4,500,000, excluding VAT and infrastructure costs.

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Yes, subject to technical and security review.

Complex SSO and related security integrations are quoted separately from KRW 50,000,000 because identity, network and approval requirements vary.

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Core on-premises workflows can be designed for restricted networks, but live web checks and external AI observations may be unavailable.

Those limitations are documented in the scope and reports.

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The public experience demonstrates Goal definition, evidence review, approval and action planning with clearly labelled sample data.

Contracted analysis uses the customer’s approved domains, questions, channels, languages and measurement period, and separates measured, unsupported and unmeasured results.