First 90 Days — Local Business AEO Roadmap
The first 90 days of local AEO follow a fixed sequence — six-platform mention baseline, accuracy and listing repairs, ethical review velocity, entity schema and llms.txt, then monthly resampling — before advanced GEO content. Most single-location operators can execute weeks 1–8 DIY; mention-rate movement typically appears over 60–120 days, not overnight, and no roadmap guarantees ChatGPT placement.
Day zero — why sequence beats shortcuts
A property manager asks ChatGPT for a commercial HVAC vendor in Tampa with 24/7 dispatch. ChatGPT names two companies. Yours is not one — despite ranking on page one of Google for related keywords.
The owner buys an "AI optimization package" that publishes ten blog posts in week one. Day 45: still invisible in ChatGPT. Root cause was never content — it was an unmerged duplicate GBP, unclaimed Apple Business Connect, and conflicting phone numbers on Yelp.
Answer Engine Optimization (AEO) for local businesses is operational marketing with a defined order of operations. Skip steps and you optimize a broken entity graph.
This 90-day roadmap is the sequence we deploy at AIrecommend.ai for new Growth clients — modular, measurable, honest about timelines. It extends what is AEO into weekly tasks. It feeds your first quarterly audit on day 90.
We will not promise ChatGPT placement by day 30. We will promise that by day 90 you will know your mention rates, fixed verifiable errors, and have a data-backed plan for the next quarter.
Roadmap overview — three phases
Days 1–30 FOUNDATION Baseline + accuracy + listings
Days 31–60 SIGNALS Reviews + entity + FAQ alignment
Days 61–90 MEASURE+SCALE Resampling + SOAV + optional GEO
| Phase | Primary KPI | Success looks like |
|---|---|---|
| Foundation | Wrong-fact count → 0 | Clean NAP graph, duplicates merged |
| Signals | Review velocity + schema valid | Themes align with buyer intents |
| Measure+Scale | Mention rate trend ↑ | SOAV baseline, prioritized Q2 queue |
Allocate 3–5 hours/week single location; 8–15 hours/week multi-location without agency support.
Days 1–7 — Baseline and inventory
Day 1–2: Six-platform mention scan
Goal: Know reality before fixing anything.
Write 8–10 buyer-intent prompts — no brand-leading questions. Examples:
- "Who's the best [category] in [city] for [specific need]?"
- "Recommend a [category] near [neighborhood] with strong reviews"
Run each prompt on:
- ChatGPT (note browsing on/off)
- Gemini
- Claude
- Perplexity
- Grok (if audience uses X)
- Google AI Overviews
Log in spreadsheet columns: Platform | Prompt | Named? | Rank | Themes | Citations | Wrong facts
Calculate mention rate per platform and combined platform coverage (how many of 6 name you at least once).
Optional: Free AIrecommend.ai scan automates this and maps gaps to modules.
Do not fix anything yet. Baseline integrity matters.
Related: how to check what ChatGPT says.
Day 3–4: Asset inventory
Document access and status:
| Asset | URL / ID | Admin access? | Issues noted |
|---|---|---|---|
| GBP | |||
| Apple BC | |||
| Bing Places | |||
| Website CMS | |||
| Yelp | |||
| Industry directories |
Identify orphan listings — profiles created by aggregators or former staff.
Day 5–7: Wrong-fact triage
From baseline sampling, list every AI error about you:
- Wrong phone, address, hours
- Services you do not offer
- "Permanently closed"
- Conflation with competitor
Tag likely source — which directory conflicts. This becomes Week 2–3 fix queue — reputation repair.
Week 1 deliverable: Baseline spreadsheet + wrong-fact log + asset inventory.
Days 8–30 — Accuracy and listing graph
Accuracy before authority. AI omits unresolved entities.
Week 2: Google Business Profile hardening
- Claim/verify primary GBP if not verified
- Merge duplicate listings via Google support or merge tool
- Primary category correct; secondary categories added
- Services list complete and plain-language
- Hours accurate including holidays
- Service area boundaries realistic — no fake geo spam
- Photos refreshed (storefront, team, work samples)
- Appointment/booking link correct
Review policy check: No gating, no incentives violating Google rules — ethical review guide.
Week 3: Apple Business Connect + Bing
Apple BC is systematically skipped and disproportionately affects Apple Intelligence / Siri paths — NAP + Apple guide.
- Claim Apple BC location
- Match NAP exactly to GBP canonical
- Categories aligned
- Showcase photos uploaded
- Bing Places claimed and synced
Log completion dates — rescans reference listing sync latency (often 2–4 weeks).
Week 4: Directory crosswalk + website footer
Build NAP crosswalk — every row must match:
| Field | GBP (canonical) | Apple | Bing | Yelp | Website | Schema |
|---|
Fix order:
- Pick canonical source (usually GBP legal name + address)
- Update website footer and contact page
- Push to directories
- Submit aggregator updates (Yext/Moz Local if used)
Category-critical directories (Healthgrades, Avvo, Houzz, etc.) — claim if missing.
Days 8–30 deliverable: Zero duplicate GBP, Apple BC claimed, NAP crosswalk green, wrong-fact sources identified for elimination.
Days 31–60 — Reviews, entity, and FAQ alignment
Universal signals that feed how AI assistants choose businesses.
Week 5–6: Ethical review velocity program
Reviews remain top-weighted evidence — 2026 review impact.
Setup:
- Define ethical ask SOP — post-service email/SMS with direct Google link; no star pre-selection
- Train staff on verbal ask at happy moment
- Target steady new reviews/month vs burst (platforms and buyers distrust spikes)
- Owner responses within 48 hours — factual, professional
Theme coaching (not scripting): Ask customers to mention what was actually true — "You explained the estimate clearly" beats "Leave us five stars."
Track:
| Week | New reviews | Avg rating | Top themes |
|---|
Realistic 30-day goal: +8–15 new Google reviews for active customer-flow business — varies by volume.
Week 7: Entity schema + llms.txt
Technical layer — entity authority guide.
- Deploy LocalBusiness / ProfessionalService JSON-LD on homepage or location page
- Include stable
@id,sameAs(GBP, Apple, LinkedIn, etc.) -
areaServedmatches real geography - Service types reflect current menu
- Publish
/llms.txt— business summary, key URLs, contact — checklist - Validate schema — fix errors before adding new types
AIrecommend.ai Growth Entity Profile module generates schema + llms.txt from onboarding with approval queue — DIY alternative is schema generators plus manual QA.
Week 8: FAQ alignment + structured data
Map top 5 buyer questions from sales calls to onsite FAQ:
- Pricing model (ranges, not bait)
- Timeline expectations
- Credentials / licensing
- Service area boundaries
- Emergency vs scheduled availability
Deploy FAQPage schema on dedicated FAQ or service pages — FAQ schema guide.
Align FAQ language with review themes and prompt library intents — models echo consistent vocabulary.
Days 31–60 deliverable: Review SOP live, schema + llms.txt deployed, FAQ with structured data, theme alignment doc.
Days 61–90 — Measurement, competition, and scale decisions
Week 9: First monthly resample
Re-run exact same prompt library from Day 1.
Compare:
| Platform | Day 1 mention % | Day 60+ mention % | Δ |
|---|
Expect noise — single resample is not verdict. Document ChatGPT browsing mode consistent with baseline.
If mention rate unchanged but wrong facts cleared, progress is real — models may need more review velocity or time to refresh retrieval.
If mention rate dropped, check competitor activity and listing regressions.
Week 10: Competitor SOAV baseline
Add 3–5 tracked competitors — names that appeared in your samples.
Calculate share of AI voice — measurement guide:
SOAV = your mentions ÷ (your mentions + competitor mentions) across prompt set.
Log citation domains when Perplexity shows sources — competitor analysis.
Identify one platform gap to prioritize Q2 — e.g., "win Perplexity" vs "win ChatGPT" based on where buyers actually convert.
Week 11: Attribution setup
Train front desk / CRM:
- Add AI sources to "how did you hear about us?"
- Log ChatGPT, Gemini, Perplexity, AI Overview separately from "Google"
- Track 30-day AI-attributed lead count
Zero-click hires may never hit analytics — zero-click guide.
Compare attribution count to mention rate trend — alignment builds internal buy-in for Q2 budget.
Week 12: Q2 plan + scale decision
Decision tree:
Mention rate ↑ + SOAV competitive → Maintain: monthly resample, review SOP, quarterly full audit
Mention rate flat + accuracy fixed → Add GEO: citable local content, press, or Dominance study
Mention rate 0% + accuracy broken → Return to Days 8–30 — do not add blogs
Multi-location variance → Per-location dashboards; consider Growth tier
No internal bandwidth → AIrecommend.ai Growth $4,997/mo or Dominance $9,999/mo
Cap Q2 at 5–7 prioritized actions — finish beats scatter.
Day 90 deliverable: Second resample report, SOAV table, attribution baseline, written Q2 plan.
Week-by-week calendar (printable)
| Week | Focus | Hours est. |
|---|---|---|
| 1 | Baseline scan + inventory | 4–6 |
| 2 | GBP merge + completeness | 3–5 |
| 3 | Apple BC + Bing | 2–4 |
| 4 | NAP crosswalk + directories | 3–5 |
| 5–6 | Review SOP + velocity | 2–3 ongoing |
| 7 | Schema + llms.txt | 3–4 |
| 8 | FAQ + FAQ schema | 3–4 |
| 9 | Resample #1 | 2–3 |
| 10 | Competitor SOAV | 2–3 |
| 11 | Attribution setup | 1–2 |
| 12 | Q2 planning | 2–3 |
Multi-location variant
Days 1–30: Per-location baseline — mention rates vary by geo; brand-level averaging hides losers.
Days 8–30: Stagger listing fixes — highest-revenue location first.
Days 31–60: Centralize schema template; localize address, phone, areaServed.
Days 61–90: Roll up SOAV by market; assign location managers P0 fixes.
6+ locations without dedicated ops: Growth tier for approval queues and rescan automation — DIY vs agency.
Realistic outcomes — honest ranges
Not guarantees — composite patterns from scans:
| Starting state | Day 90 typical pattern |
|---|---|
| 0% mentions, bad NAP | Wrong facts reduced; mentions 5–20% on 1–2 platforms |
| 10% mentions, decent reviews | 15–35% combined; SOAV gain if competitors static |
| Strong SEO, 0% AI | Listing/entity fix unlocks first mentions 60–90 days |
| Saturated legal/medical | Slow movement; needs reviews + citable content |
Factors: category competitiveness, metro size, competitor agency activity, model updates.
Red flag vendors: "Day 14 ChatGPT guarantee" — exit.
What NOT to do in the first 90 days
- Publish 20 AI-generated blog posts before NAP clean
- Buy fake reviews or pay-for-play "AI directory" listings
- Change prompt library weekly — trends become unreadable
- Optimize only ChatGPT ignoring Perplexity/Gemini — 11% overlap
- Rebrand website without updating graph
- Skip Apple BC because "we are not an iPhone shop"
- Treat AEO as one-time project — monthly resampling starts day 60 and continues
Advanced optional (days 45–90 only if basics green)
If accuracy + reviews + schema complete and bandwidth remains:
- Local data mini-study for Perplexity retrieval (Dominance pattern)
- Podcast guesting on geo-relevant shows — strategy
- Press release with verifiable local hook — not wire spam
- Service page expansion with buyer-intent FAQs per neighborhood (avoid thin doorway pages)
Do not start advanced GEO if P0 listing work incomplete — GEO vs AEO decision.
Integrating with existing SEO
AEO runs parallel, not replacement:
| SEO (continue) | AEO (add) |
|---|---|
| Local pack rank | Mention rate |
| Organic traffic | SOAV |
| Backlinks | Citation domains in AI answers |
| Content keywords | Buyer-intent prompt library |
Divergence diagnosis:
- Rank well, 0% mentions → entity/listing gap
- Mentions up, traffic flat → zero-click — optimize calls
- Both up → rare win — protect with quarterly audits
Framework: AEO vs GEO vs SEO.
Budget framing for 90 days
| Path | Cash cost | Outcome risk |
|---|---|---|
| DIY owner time | ~40–60 hours | Low cash, high consistency requirement |
| Tools (review, schema) | $50–300/mo | Moderate |
| Freelance listing cleanup | $500–2k one-time | Quality varies |
| AIrecommend.ai Growth | $4,997/mo × 3 | Module execution + rescans — no placement guarantee |
| AIrecommend.ai Dominance | $9,999/mo × 3 | Adds studies, press, wrong-fact ops |
Full budget context: local AI marketing budget 2026.
Day 90 handoff to quarterly rhythm
On day 90, schedule:
- Monthly: Prompt resample (2 hours)
- Quarterly: Full six-workstream audit — checklist
- Annual: Prompt library refresh, competitor set review
Export baseline spreadsheet to shared drive — institutional memory prevents restart when staff turns over.
Bottom line
The first 90 days of local AEO are sequential: measure, fix accuracy, claim Apple and Bing, build ethical review velocity, deploy schema and FAQ alignment, resample mentions, benchmark competitor SOAV, set attribution, plan Q2.
Skip the sequence and you will wonder why content did not work. Follow it and you will have honest baselines — the prerequisite for every improvement that follows.
No roadmap guarantees ChatGPT names you by day 90. Every roadmap that fixes verifiable signals gives you a fighting chance in an AI-first local market.
Free scan to start Day 1 · What is AEO · Why ChatGPT skips you.
Frequently asked questions
What should I do first in an AEO program?
Establish a six-platform mention baseline with buyer-intent prompts, then fix NAP conflicts and duplicate listings before investing in content. AI systems skip or misstate businesses they cannot resolve confidently.
How long until AEO shows results for local businesses?
Listing and accuracy fixes can reduce wrong-fact errors within weeks; mention-rate gains often take 60–120 days in competitive markets. Resample monthly on a fixed prompt library — timelines vary and are not guaranteed.
Can I complete the 90-day AEO roadmap myself?
Yes for most single-location businesses with 3–5 hours per week. Multi-location operators or teams with chronic wrong AI facts often need AIrecommend.ai Growth ($4,997/mo) or Dominance ($9,999/mo) for execution bandwidth.
Should I prioritize ChatGPT or Google in the first 90 days?
Neither exclusively. Fix universal signals — GBP, Apple BC, reviews, schema — that feed all platforms. Measure mention rate per engine separately; low cross-platform overlap means wins on one do not automatically transfer.
What belongs in days 61–90 versus days 1–30?
Days 1–30 are baseline plus accuracy. Days 31–60 add reviews, schema, and FAQ alignment. Days 61–90 add resampling, competitor SOAV tracking, and optional citable content — not speculative tactics before measurement stabilizes.