AI search visibility needs proof.

This case-style playbook shows how an anonymized B2B SaaS and a local service brand used the AISO Hub workflow to win AI citations, fix inaccuracies, and drive leads.

Here is the direct answer up front: audit entities and schema, rewrite priority pages with answer-first structures, validate hreflang and performance, run prompt panels weekly, and tie changes to conversions.

You’ll see the steps, timelines, KPIs, and lessons so you can replicate the wins.

Keep our AISO vs SEO guide as the pillar while you read.

Context and objectives

  • Markets: EN/PT (SaaS) and EN/PT/FR (local service).
  • Baseline: Strong classic SEO but almost no AI citations; outdated schema; mixed hreflang; no prompt monitoring.
  • Goals:
    • Appear in AI Overviews, Perplexity, and Copilot for revenue queries.
    • Fix pricing/security inaccuracies in ChatGPT Search.
    • Increase demo bookings (SaaS) and calls/forms (local service).
  • KPIs: Inclusion, citation share, accuracy, sentiment, conversions on cited pages, time-to-fix.

Phase 1: Diagnostic (Weeks 1–2)

  • Ran baseline prompt panels (80 prompts per market) across AIO, Perplexity, Copilot, ChatGPT Search; logged citations and wording.
  • Crawled site for schema errors, hreflang issues, and performance bottlenecks; found 120 schema errors and hreflang mismatches on 30% of PT pages.
  • Entity mapping revealed inconsistent product naming across locales and missing sameAs for authors.
  • Support content buried answers; no FAQ/HowTo schema; tables below the fold.

Key findings

  • Wrong-language citations in Copilot due to hreflang drift.
  • ChatGPT misquoted pricing; Perplexity omitted the brand for “best” prompts.
  • Schema missing about/mentions; Organization/Person incomplete; logo 404 in PT.
  • Slow LCP on blog template (>3s) hurt eligibility.

Phase 2: Strategy and backlog (Week 3)

  • Built prompt libraries by cluster (pricing, comparisons, integrations, support, security) per locale.
  • Prioritized 25 URLs: pricing, “vs”, integration guides, and top support FAQs.
  • Set KPIs: +15 points inclusion, +10 points citation share, 0 inaccuracies on pricing/security, +10% conversions on cited pages in 90 days.
  • Defined SLAs: schema errors fixed in 48h, inaccuracies in 72h, performance regressions paused immediately.

Phase 3: Execution (Weeks 4–10)

Content and structure

  • Rewrote leads to answer-first (≤100 words) with proof points and sources.
  • Added comparison tables above the fold; added verdict lines for “vs” pages.
  • Built FAQs and HowTo steps; added glossary blocks for niche terms.

Schema and entities

  • Implemented Article + FAQ/HowTo + Product/LocalBusiness where relevant; filled about/mentions for key entities.
  • Completed Organization and Person schema with sameAs (LinkedIn, Crunchbase) in all locales; fixed logo 404s.
  • Added LocalBusiness schema with geo/areaServed for service pages; aligned NAP with Bing Places/GBP.

Technical and performance

  • Fixed hreflang/canonical drift; published locale sitemaps with lastmod.
  • Improved LCP on blog template from 3.2s to 1.7s (image compression, deferred scripts).
  • Enabled schema linting in CI; blocked deploys on critical errors.

Monitoring and iteration

  • Ran weekly prompt panels; logged citations, accuracy, and sentiment.
  • Tracked conversions on targeted pages with UTMs and dashboards; annotated releases.
  • Fixed inaccuracies fast: pricing misquotes resolved in 3 days after source updates and FAQ additions.

Results (by Week 12)

  • Inclusion: AIO +19 points (EN), +23 points (PT); Perplexity +17 points (EN), +21 points (PT); Copilot +15 points (EN/PT).
  • Citation share: “Best” prompts from 8% to 22% (EN) and 6% to 19% (PT); “vs” from 10% to 25% average.
  • Accuracy: Pricing/security inaccuracies dropped to zero in retests; wrong-language citations eliminated.
  • Conversions: Demo requests on cited SaaS pages +14%; service calls/forms from cited pages +18%.
  • Operational: Time-to-fix for schema errors down to <48h; answer-first compliance on new pages at 95%+.

Artifacts you can reuse

  • Prompt panel sheet: Queries by intent, persona, locale; columns for engine, citation URLs, wording, sentiment, accuracy, notes.
  • Changelog: Date, URL, change, owner, prompts retested, outcome (lift/no change/decline).
  • Brief template: Target prompt, lead draft, proof, schema types, about/mentions entities, table/FAQ requirements, localization notes, risks, KPIs.
  • QA checklist: Lead length, sources, schema validation, hreflang, performance, anchors, update note.

What didn’t work (and why)

  • Over-long leads (120–140 words) reduced citations; trimmed back to ~90 words.
  • Copying EN schema to PT without localizing descriptions triggered errors and mistrust; fixed with locale-specific fields.
  • Adding too many FAQs (10+) caused clutter and validation warnings; narrowed to 4–6 high-intent questions.
  • Relying on AI translations without human QA led to entity confusion; switched to hybrid AI + editor workflow.

Lessons learned

  • Hreflang accuracy is non-negotiable; wrong-language citations disappear once fixed.
  • Answer-first leads plus tables above the fold drive faster citation gains than deeper content rewrites alone.
  • Schema breadth (FAQ/HowTo/Product/LocalBusiness) plus about/mentions accelerates inclusion.
  • Freshness signals matter: visible update notes and dateModified aligned with actual edits improved retention of citations.
  • Weekly prompt panels and fast fixes keep accuracy high and prevent brand risk.

Applying this to your site: step-by-step

  1. Baseline: Run prompt panels (EN/PT/FR if relevant); log citations and inaccuracies.
  2. Fix foundation: hreflang, sitemaps, Core Web Vitals, Organization/Person schema.
  3. Rewrite top 20 pages: answer-first leads, tables, FAQs, proof blocks; validate schema.
  4. Monitor weekly: inclusion, share, accuracy; fix errors within 72h.
  5. Expand: product/pricing/support pages and comparisons; localize; add LocalBusiness/Product schema.
  6. Report: monthly trends and quarterly ROI; tie to conversions on cited pages.
  7. Iterate: A/B tables and lead length; grow prompt library; secure local mentions and reviews.

Mini-case: local service (PT/EN/FR)

  • Problem: Directories dominated AI answers; prices/services outdated in PT/FR.
  • Actions: LocalBusiness schema, localized FAQs, fresh reviews with dates, and answer-first service pages; fixed hreflang.
  • Results (10 weeks): Copilot citations shifted from directories to brand; “perto de mim/près de chez moi/near me” prompts included correct locale pages; form submissions +18%.

Mini-case: B2B SaaS

  • Problem: ChatGPT misquoted pricing; Perplexity ignored the brand on “best”.
  • Actions: Answer-first pricing and comparison pages with tables; FAQ/HowTo schema; Organization/Person cleanup; integration FAQs.
  • Results (12 weeks): Pricing inaccuracies removed; Perplexity inclusion +17 points; demo conversions +14% on cited pages.

How to present this to stakeholders

  • Start with before/after screenshots of AI answers citing (or ignoring) your brand.
  • Show a simple table: inclusion, citation share, inaccuracies, conversions on cited pages (baseline vs now).
  • Outline the workflow steps taken and time-to-fix improvements.
  • Share the backlog priorities and expected metric lift for next quarter.
  • Keep it short: 1–2 slides per section; include a clear ask for resources or approvals.

Risks and mitigations

  • YMYL: Keep expert review and disclaimers; track accuracy weekly.
  • Wrong-language citations: Monitor hreflang; fix and re-run prompts immediately.
  • Performance regressions: Block deploys on poor LCP/INP; enforce budgets.
  • Schema drift: Automate linting; block deploys on errors.
  • Over-automation: Keep human QA on leads, sources, and schema for accuracy and tone.

Backlog template (case-ready)

  • Foundation: hreflang, sitemaps, Core Web Vitals fixes; Organization/Person schema; sameAs completion.
  • Content: answer-first rewrites for pricing, “vs”, support, and integration pages; add proof blocks and tables.
  • Schema: FAQ/HowTo/Product/LocalBusiness/Breadcrumb validation; about/mentions filled.
  • Authority: local/industry mentions, reviews, and PR pushes aligned to entities.
  • Measurement: prompt panels, dashboards, alerts, accuracy log; A/B tests.
  • Governance: changelog, SLAs, release checklist, and incident playbook.

Detailed timeline (SaaS example)

  • Week 1–2: Baseline prompts and crawl; fix critical 5xx/4xx and logo/schema asset errors; set dashboards.
  • Week 3: Prioritize URLs; build briefs; finalize glossary/entities; fix hreflang on top pages.
  • Week 4–6: Publish 10 answer-first rewrites (pricing, “vs”, integration hubs); apply FAQ/HowTo schema; improve LCP.
  • Week 7–8: Localize PT pages; add LocalBusiness where relevant; expand prompt panels; address inaccuracies.
  • Week 9–10: Experiments (table placement, lead length); add proof blocks/screenshots; secure two mentions.
  • Week 11–12: Consolidate gains; present results; set next quarter backlog (long-tail prompts, new features).

Metrics table (illustrative)

  • Inclusion: EN 42% → 61%; PT 28% → 51%.
  • Citation share on “best”: 8% → 22% (EN); 6% → 19% (PT).
  • Accuracy: pricing errors 6 → 0; wrong-language citations 11 → 0.
  • Conversions on cited pages: +14% demos (EN), +9% demos (PT); +18% calls/forms (local service).
  • Time-to-fix: schema 5 days → 2 days; accuracy 10 days → 3 days.

Documentation artifacts (redacted examples)

  • Before/after screenshots: AI Overview and Perplexity answers citing competitor vs brand after fixes.
  • Prompt lists: 80 prompts tagged by intent and locale; includes pricing/security questions that drove early wins.
  • Change logs: Per URL entries with date, change, prompts retested, and outcomes.
  • QA sheets: Checkboxes for leads, schema, hreflang, performance, and sources.

What to watch post-launch

  • Decay: Some citations fade after 4–6 weeks; schedule refreshes for stats/pricing.
  • Engine changes: Annotate dashboards with model updates; retest high-value prompts.
  • Competitor moves: Monitor new mentions or content that could displace you; adjust prompts and pages.
  • Sentiment: Watch for negative framing; reinforce with updated proof and mentions.

Scaling across verticals

  • Healthcare: Add reviewer names/credentials, disclaimers, and links to official guidelines; tighter accuracy logs.
  • Finance: Regulatory disclaimers, rate update cadence, and secured PDFs with HTML summaries; avoid speculative claims.
  • Ecommerce: Product/Offer schema with daily price updates; comparison tables; reviews with dates; local availability.
  • B2B services: Case summaries, service area clarity, LocalBusiness schema, and testimonial schema; track “near me” prompts.

Guidance for smaller teams

  • Start with 10–15 prompts and 5–10 pages; focus on pricing/“vs” pages and one support cluster.
  • Use lightweight scripts or manual logs; keep screenshots in a dated folder.
  • Reuse templates ruthlessly; one brief/checklist fits most URLs.
  • Prioritize schema and answer-first leads before deep content rewrites; fastest gains.
  • Set a weekly one-hour block for panel + fixes; consistency beats volume.

Example leadership one-pager

  • Objective: “Win AI citations on revenue queries and eliminate pricing inaccuracies.”
  • Actions: Rewrote 20 pages, added FAQ/HowTo schema, fixed hreflang, improved LCP, ran weekly panels.
  • Results: +19–23 points inclusion, 0 pricing errors, +14% demos, +18% calls/forms.
  • Next: Expand long-tail prompts, add LocalBusiness to new locations, A/B table placement.
  • Needs: Keep schema linting in CI; resource for PT/FR localization QA.

Post-mortem for failed experiments

  • Table below the fold did not improve citations; moving it up did.
  • Long-form leads reduced clarity; concise leads performed better.
  • Overly generic FAQs got ignored; specific, sourced FAQs improved citations.
  • Automation without human QA introduced translation errors; reinstated human checks.

Five screenshots to capture (describe your own)

  • AI Overview before/after showing competitor citation replaced by brand.
  • Perplexity answer highlighting updated table row with correct pricing.
  • Copilot citation showing localized PT page instead of EN.
  • Dashboard panel with inclusion/citation share trend lines annotated with releases.
  • Analytics view showing conversion lift on cited pages post-change.

Keeping momentum after the first 90 days

  • Rotate clusters as gains stabilize; move to integrations, support, and long-tail.
  • Refresh prompt libraries monthly; add sales/support questions and seasonal intents.
  • Review changelog quarterly; double down on levers that moved metrics (schema breadth, performance, mentions).
  • Publish internal win notes with before/after and lessons to onboard new team members.

Metrics to guard against regression

  • Inclusion and citation share by cluster/locale.
  • Accuracy incidents per month; time-to-fix.
  • Schema error rate and time to resolve.
  • Hreflang error count; wrong-language citation occurrences.
  • LCP/INP by template.
  • Freshness: % of priority pages updated in last 45 days.
  • Conversions on cited pages; assisted conversions linked to visibility spikes.

Building your own case library

  • Standardize structure: context, problem, actions, metrics, timeline, lessons.
  • Redact names if needed but keep percentages and dates for credibility.
  • Include at least one local-market example (PT/FR) and one B2B example.
  • Link cases from service pages and pillars; use descriptive anchors.
  • Update cases quarterly; show durability of wins.

If results stall

  • Re-check foundation: crawlability, schema errors, hreflang, performance.
  • Strengthen entities: about/mentions, glossary, sameAs.
  • Secure new mentions/reviews; assistants often need fresh authority signals.
  • Test different lead lengths, table placements, and FAQ sets.
  • Increase monitoring cadence; engines may be shifting models.

Budget and procurement tips

  • Show cost of inaction: missed citations on revenue prompts and misquoted prices.
  • Quantify gains: demo/call lifts, reduced inaccuracies, faster time-to-fix.
  • Highlight efficiency: templates, CI linting, and dashboards reduce manual QA.
  • Bundle asks: schema automation + localization QA + monitoring saves time across teams.

Scaling across verticals

  • Healthcare: Add reviewer names/credentials, disclaimers, and links to official guidelines; tighter accuracy logs.
  • Finance: Regulatory disclaimers, rate update cadence, and secured PDFs with HTML summaries; avoid speculative claims.
  • Ecommerce: Product/Offer schema with daily price updates; comparison tables; reviews with dates; local availability.
  • B2B services: Case summaries, service area clarity, LocalBusiness schema, and testimonial schema; track “near me” prompts.

Guidance for smaller teams

  • Start with 10–15 prompts and 5–10 pages; focus on pricing/“vs” pages and one support cluster.
  • Use lightweight scripts or manual logs; keep screenshots in a dated folder.
  • Reuse templates ruthlessly; one brief/checklist fits most URLs.
  • Prioritize schema and answer-first leads before deep content rewrites; fastest gains.
  • Set a weekly one-hour block for panel + fixes; consistency beats volume.

Example leadership one-pager

  • Objective: “Win AI citations on revenue queries and eliminate pricing inaccuracies.”
  • Actions: Rewrote 20 pages, added FAQ/HowTo schema, fixed hreflang, improved LCP, ran weekly panels.
  • Results: +19–23 points inclusion, 0 pricing errors, +14% demos, +18% calls/forms.
  • Next: Expand long-tail prompts, add LocalBusiness to new locations, A/B table placement.
  • Needs: Keep schema linting in CI; resource for PT/FR localization QA.

Post-mortem for failed experiments

  • Table below the fold did not improve citations; moving it up did.
  • Long-form leads reduced clarity; concise leads performed better.
  • Overly generic FAQs got ignored; specific, sourced FAQs improved citations.
  • Automation without human QA introduced translation errors; reinstated human checks.

How AISO Hub can help

We turn these steps into repeatable wins.

  • AISO Audit: Baseline AI visibility, schema, entities, and performance; prioritized roadmap.

  • AISO Foundation: Implement templates, schema, hreflang, and governance so teams ship fast.

  • AISO Optimize: Execute sprints, run prompt panels, and test layouts to grow citation share.

  • AISO Monitor: Dashboards, alerts, and accuracy tracking that tie to conversions.

Conclusion

This case study shows that AISO gains come from disciplined execution: fix foundations, write answer-first, validate schema, monitor AI answers, and respond fast.

You now have timelines, KPIs, and checklists to replicate the wins.

Start with a baseline, prioritize high-intent pages, and measure every change.

Align with the AISO vs SEO pillar so classic rankings and AI citations reinforce each other.

If you want a partner to run this with you, AISO Hub is ready to audit, build, optimize, and monitor so your brand shows up wherever people ask.