AI answer visibility tracking workspace

For

Brand, SEO and communications teams tracking how their brand appears in AI-generated answers

Solves

Brands cannot see how often AI assistants mention them, which prompts trigger mentions, which competitors appear instead, or which sources shape the answers.

Delivers

Reviewed AI visibility reports linked to source evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Replace several rented AI-visibility subscriptions with one owned workspace that tracks brand mentions across AI platforms and turns them into reviewed, client-ready reports.

1. Track brand mentions across multiple AI platforms.
2. Analyze which prompts trigger mentions and how positioning changes.
3. Track competitor and alternative recommendations in AI answers.
4. Map cited domains and pages behind each answer.
5. Compute share of voice against competitors.
6. Group prompts by customer journey stage.
7. Generate client-ready reports from tracked data.
8. Run AI-readiness audits on crawler access and page extractability.
9. Average repeated snapshots to smooth response variability.
10. Store brands, prompts, competitors and history in one dashboard.
11. Extract sentiment signals from AI responses.
12. Support a content engineering and approval workflow.
13. Connect to content systems and internal agents by API.
14. Use real AI crawler traffic data alongside estimates.
15. Cover multiple languages and geographic markets.
16. Benchmark against industry and brand datasets.
17. Highlight where models disagree on top recommendations.
18. Score honorific tone for Japanese-language mentions.
19. Produce a prioritized fix list from scan results.
20. Track which AI agents visit the site and where access is blocked.
21. Cross-reference agent visits and citations with human traffic.
22. Give technical recommendations for rendering, load times and robots rules.

Everything these tools do, in one app

• Multi-model AI visibility tracking Monitors how often a brand appears in AI-generated answers across multiple AI platforms.Found in AI Search Console, PromptScout, Pendium and 3 more
• Prompt-level analysis Shows which specific prompts or queries trigger brand mentions and how positioning changes.Found in AI Search Console, PromptScout, Atyla and 3 more
• Competitor visibility tracking Identifies competitors or alternative recommendations that AI answers surface instead of your brand.Found in AI Search Console, PromptScout, Pendium and 2 more
• Citation mapping Identifies the domains and pages cited in AI answers to show which sources shape responses.Found in AI Search Console, PromptScout, Pendium and 1 more
• Share of voice metrics Measures the proportion of AI mentions a brand receives relative to competitors.Found in AI Search Console
• Prompt grouping by journey stage Organizes prompts by customer journey stage for structured analysis across funnel phases.Found in AI Search Console
• Client-ready reporting Generates reports directly from tracked data, removing manual screenshot-and-spreadsheet workflows.Found in AI Search Console, PromptScout
• AI-readiness audit Checks crawler access, page extractability, and discoverability to identify technical issues before monitoring.Found in PromptScout
• Aggregated snapshot sampling Smooths out variability in AI responses by averaging repeated snapshots to report trends.Found in PromptScout
• Centralized dashboard Stores monitored brands, prompts, competitor lists, and historical reports in one place.Found in PromptScout, Pendium
• Sentiment analysis Extracts sentiment signals from AI responses to understand how brands are portrayed.Found in Pendium
• Content engineering workflow Provides a hosted platform for engineering, approving, and publishing agent-focused content.Found in Pendium
• API and integrations Connects with existing content engineering systems or feeds results into internal agents.Found in Pendium, Siteline
• Real crawler traffic data Uses actual AI crawler traffic rather than estimates to provide concrete visibility data.Found in Atyla, Siteline
• Multilingual coverage Works across multiple languages and geographic markets to reveal localized AI visibility patterns.Found in Atyla, Japanly AEO
• Industry benchmarking Compares brand visibility against a large dataset of industries and brands.Found in The AI 500
• Model disagreement analysis Highlights when different AI assistants differ on top recommendations.Found in The AI 500
• Honorific tone scoring Measures the Japanese politeness register (敬語) in AI mentions to assess cultural appropriateness.Found in Japanly AEO
• Prioritized fix list Generates a list of recommended actions ordered by what to address first based on scan results.Found in Japanly AEO
• Agent and bot traffic tracking Identifies which AI agents visit your site, frequency, and where access is blocked.Found in Siteline
• Correlation with human traffic Cross-references agent visits and citations with human visits using UTMs and referral headers.Found in Siteline
• Technical recommendations Provides diagnostics for server-side rendering, load times, robots rules, and other factors affecting agent readability.Found in Siteline

What goes in, what comes out

The workflow

1. In
   Start with

   Monitored prompts, model responses, citation pages, crawler logs and competitor lists

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect monitored prompts

4. 3

   Model responses

5. 4

   Citation pages

6. 5

   Crawler logs and competitor lists

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed AI visibility reports linked to source evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Sampling covers a fixed prompt set and model list; final interpretation and client claims remain analyst-reviewed. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Brand and prompt setup, Visibility dashboard, Report builder and delivery. Use a thumbnail gallery for tracked brands and prompt sets, a large central dashboard for mention rates and share of voice, and a right-hand panel for citations, sentiment and competitor detail. Let users compare periods and models side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or prompt. Make the task-specific outcome reviewed AI visibility reports linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, prompt versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Brand-owned prompt lists, authorized analytics exports and permitted research sources. Cloud storage, content engineering systems and internal agent endpoints. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: track brand mentions across multiple AI platforms; analyze which prompts trigger mentions and how positioning changes. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will brand, SEO and communications teams tracking how their brand appears in AI-generated answers use it to solve "brands cannot see how often AI assistants mention them, which prompts trigger mentions, which competitors appear instead, or which sources shape the answers"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery.
4. Measure, then decide. Track tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed prompt set and model list; final interpretation and client claims remain analyst-reviewed. Implement one approved input format, a bounded representative case set and the first two task modules: track brand mentions across multiple AI platforms; analyze which prompts trigger mentions and how positioning changes. Support the third module with operator review: track competitor and alternative recommendations in AI answers. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed AI visibility reports linked to source evidence. Retain the explicit scope boundary: One fixed prompt set and model list; final interpretation and client claims remain analyst-reviewed.

What the build depends on. Prompt upload and preview, asynchronous model queries, editable version history, reviewer access and tested export formats. High-fidelity tracking requires stable model access and specialist analyst QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed prompt set and model list; final interpretation and client claims remain analyst-reviewed.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined prompt and brand package. Offer a monthly tracking allowance after repeat demand. Quote complex multilingual or custom-integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed AI visibility reports linked to source evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Replace several rented AI-visibility subscriptions with one owned workspace that tracks brand mentions across AI platforms and turns them into reviewed, client-ready reports. Demonstrate a concrete reviewed AI visibility reports linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Brand, SEO and communications teams tracking how their brand appears in AI-generated answers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed AI visibility reports linked to source evidence from a small authorized input set, with a transparent calculation of tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery and no promised savings.

The first 30 days

1. Week 1: interview five brand, SEO and communications teams tracking how their brand appears in AI-generated answers and inspect a recent example of brands cannot see how often AI assistants mention them, which prompts trigger mentions, which competitors appear instead, or which sources shape the answers.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed AI visibility reports linked to source evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved prompt sets, model lists and review examples, together with reliable delivery for a narrow marketing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for brand, SEO and communications teams tracking how their brand appears in AI-generated answers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI Search Console, PromptScout, Pendium, Atyla, The AI 500, Japanly AEO and Siteline are what buyers use today. Compare this product with the buyer's present method on tracked prompts with stable brand mentions per reporting cycle and report corrections after client delivery. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model query attempts, crawler log processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed AI visibility reports linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand accuracy, source attribution, quotation accuracy and usage permissions. Analysts approve substantive claims and publication scope. One fixed prompt set and model list; final interpretation and client claims remain analyst-reviewed. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.