geolocal-io/docs/ALTERNATE_BUSINESS_PROPOSAL.md

# Alternate business proposal: North Star via accurate AI presence

**Branch:** `planning-selfserve-audit`  
**Status:** Alternate framing — working draft 2026-10-01 (refocused same day)  
**Relationship to SoT:** Not a replacement for [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md). That file stays the product architecture SoT. This proposal is a sharper commercial ladder to the same North Star. Amend or kill when Ty locks or rejects it.

---

## North Star (what every section serves)

From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md): **enable local businesses to be discovered and recommended by AI assistants.**

On this branch that intent is scored as two owner-facing outcomes in the concept SoT:

- **Presence** — are you on the map wherever discovery happens (majors, umbrellas, and the copies assistants lean on), with consistent identity and categories?  
- **Recommendability** — if you are on the map, would an assistant pick you for a real genre ask — and describe you **accurately**?

Longer-term destination in the SoT (not v1): an **industry graph**. Version one earns that right by making Presence and Recommendability true in today’s assistants, with owner consent.

This proposal’s only job: a path that moves Presence and Recommendability when AI answers are missing or wrong because **stale directory / aggregator data** feeds those answers.

---

## Problem → North Star gap

r/localseo practitioners keep hitting the same failure modes that break the North Star:

- **Not discovered.** Reported experiments (e.g. six engines × ~50 locals) claim most shops are invisible to most assistants even with strong Google rankings — Presence fails in the AI layer.  
- **Not recommended — or recommended wrong.** ChatGPT and Gemini pull from **different pools** (Gemini more GBP/Maps-grounded; ChatGPT more web-search fan-out). Reviews alone don’t guarantee a cite. Stale hours and wrong services from random directories show up in answers — Recommendability fails on **accuracy**, not only mention count.  
- **Owners can’t see the gap.** Tools feel unreliable; people run manual prompt grids. Without an accuracy-aware audit, they can’t tell if they are present or recommendable to AI.

Root mechanism (same black box as the concept SoT): a cloud of unreconciled NAP copies. Assistants pick from the fog. Localeze’s paid hub and Completeness Score optimize **their** profile form — they do not measure AI Presence/Recommendability across the web.

---

## Solution → how we move the North Star

### Audit: measure Presence and Recommendability where AI looks

Owner-initiated (web first):

1. Stakeholder verify.  
2. Prompt major assistants with fixed buyer asks; record variance.  
3. Score **Presence** (identified correctly across claim surfaces and stomped copies) and **Recommendability** (named when it matters; facts in the answer match claimed truth).  
4. Prefer “mentioned vs recommended vs **misstated**” over vanity mention counts.

That is the North Star scoreboard applied to live AI answers — not Localeze completeness, not citation-pack vanity.

### Ticket set: fix the sources that break Presence and Recommendability

One button mints correction tickets (field / wrong / right / **GBP or claimed truth as proof**).

**Lane A — umbrellas** (stop the fog regenerating): [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/), [Localeze / TransUnion](https://neustarlocaleze.biz/) (~$99/yr paywall; no free suggest-edit; 30–90 day monthly partners), [Foursquare Places](https://foursquare.com) (Placemaker suggest-edit; AI-adjacent POI pipe — partnership ≠ proven ranking lever).

**Lane B — majors** (own databases assistants also trust): [GBP](https://business.google.com) (proof hub), [Apple Business Connect](https://business.apple.com), [Bing Places](https://www.bingplaces.com), [Yelp](https://www.yelp.com) suggest-edit.

GBP does not outbound-syndicate NAP. Umbrella-only leaves Core 3 / Yelp wrong; majors-only leaves Layer 1 feeds stomping the claim. Both lanes exist so Presence sticks and Recommendability isn’t fed lies.

**Held multi-platform verification** (to architect): corrections that survive the next stale feed — the moat under lasting Presence.

**Success check against the North Star:** re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up.”

---

## Differentiators → only vs the North Star

| Player | Why they don’t finish the North Star alone |
| --- | --- |
| Localeze / TransUnion | Paid Lane A fan-out + hub Completeness Score; no AI-answer Presence/Recommendability audit; no Lane B ticket set |
| Yext / Uberall / BrightLocal | Strong pipes into publishers; still not “discovered and recommended accurately by assistants” as the measured outcome |
| Citation / AI-mention tools | Mismatch or mention counts; weak loop to tickets with GBP proof; rarely check answer **accuracy** |

We win if we are the shortest path from “AI can’t find / trust / correctly describe me” to “Presence and Recommendability hold after re-check.”

---

## Open questions (each must serve the North Star)

1. Promise wording: “Am I discoverable by AI?” vs “Does AI describe me correctly?” — which better sells Presence + Recommendability?  
2. Which assistants do we prompt in v1 so Recommendability measurement is honest without overclaiming?  
3. How automated is “accuracy” vs owner compare-to-claim — enough to move Recommendability without fake precision?  
4. Localeze / Data Axle filing: paid add-on, referral, or only via pipes the owner already has — without pretending free Lane A exists?  
5. Foursquare Managed Users in v1 or later — does white-label materially improve Presence on AI-facing POI graphs?  
6. Keep this file as GTM ladder and the concept SoT as architecture — or merge and delete this alternate?

---

## Capture

Refocused 2026-10-01 so every section ladders to BUSINESS_PLAN North Star intent + concept Presence/Recommendability. Evidence from Localeze research and r/localseo; statistics are practitioner-reported, not independently replicated.

**Research brief appended 2026-10-01; North Star preamble + plain-English muffin-shop section added same day** — verbose evidence synthesis below (section RESEARCH BRIEF). PDF export: [`docs/research-brief.pdf`](./research-brief.pdf). Does not replace North Star / Problem / Solution / Differentiators / Open questions; amends Capture with this date stamp only.

---

## RESEARCH BRIEF

**Title:** GEOLOCAL Research Brief — evidence behind North Star Presence + Recommendability  
**Date:** 2026-10-01  
**Branch:** `planning-selfserve-audit`  
**Relationship:** Append-only evidence layer for this alternate proposal. Does not replace the concept SoT ([`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md)). Labels below: **practitioner-reported** (r/localseo / agency anecdotes), **vendor** (product marketing or vendor research), **primary study** (named methodology / published research). Flag **UNVERIFIED** where community claim or secondary paraphrase lacks a clean primary cite.

### North Star (how to read this brief)

The North Star for GEOLOCAL is **agent-era local SMB discoverability**: local businesses **discovered and recommended by AI assistants**. That intent (from [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md)) is scored on this branch as two outcomes:

- **Presence** — being present and accurately represented wherever a local business is discovered (majors, umbrellas, directories, and the copies assistants lean on).
- **Recommendability** — would an AI pick you for a real buyer ask, and would it **get you right** (not just mention you)?

Everything below is evidence gathered **in service of that North Star** — what practitioners and vendors say moves Maps/calls vs what actually bears on AI Presence and Recommendability, what is waste, and where the proof is thin. It is not a general local-SEO encyclopedia; read every finding against “does this help an owner become discoverable and accurately recommended by agents?”

### In plain English (the muffin shop)

Imagine Maya runs a small muffin shop. She updates her Google listing with the new phone number and weekend hours. She thinks she is done.

She is not done. Yelp still shows last year’s number. Bing Places still shows last year’s hours. A dozen smaller directories — and the big data companies that quietly feed them — still have the old copy. When someone asks an AI assistant “where’s a good bakery near me?” that assistant often leans on those scattered copies. Sometimes Maya does not show up at all. Sometimes she shows up with the wrong number. Customers lose trust; Maya never sees why.

**The pain, in human terms:** fixing Google is not the same as being findable and accurately described wherever people — and AI — look.

**The fix, in human terms:** one button that (1) checks what AI assistants actually say about the muffin shop, (2) finds every stale copy, and (3) files the correction on each platform automatically. Maya never fills out a directory form. She confirms the truth once; the product does the chasing.

**How we apply that to the product:**

1. **Lead with the AI-accuracy audit** — the hook Maya can feel. “Here’s what ChatGPT / Gemini / others say about you — found, recommended, or wrong.” That is Presence and Recommendability made visible.
2. **Use the ticket set as the engine** — Lane A umbrellas (the data companies that fan out into many directories) plus Lane B majors (Google, Apple, Bing, Yelp, and the other big claim surfaces). The audit finds the stale copies; the tickets fix them with Maya’s claimed truth (and Google as proof when it helps).
3. **Measure success the way the North Star asks** — is the muffin shop findable, and is it accurately represented? Re-ask the same AI questions after corrections. Do not celebrate a directory “completeness” score that Maya’s customers never see.

Keep Maya in mind while reading the evidence below. The tables and studies are the receipts behind this story — not a different product.

### Pain

Local owners and agencies are stuck between two scoreboards that do not agree.

1. **Maps / pack pain is familiar and call-dense.** Practitioners report large call and pack swings from GBP completeness, primary category, review velocity, and citation cleanup — but those wins are often Maps-lane attributions, not proof that ChatGPT / Perplexity / Gemini will name the shop correctly.
2. **AI pain is selective and accuracy-fragile.** Primary studies (SOCi LVI 2026, Insites Jul 2026, BrightLocal Local AI Visibility Study Sep 2026) show assistants recommend far fewer locations than the 3-Pack, disagree with each other, persist only ~half the time on re-prompt, and frequently misstate NAP even when they “know” the business.
3. **Owners cannot see the gap.** Localeze Completeness, citation-count tools, and vibe-check prompt grids do not measure Presence (are you on the claim surfaces assistants lean on?) or Recommendability (would an assistant pick you *and* describe you accurately?). Selling “cleanup ROI” as if aggregator correction alone moves AI answers lacks published pack/call studies.

Practitioner snapshot (r/localseo; full thread fetch often blocked — treat as **practitioner-reported**, not replicated):

| Claim (label) | Source thread (reddit) | Notes |
| --- | --- | --- |
| ~73% of shops invisible to 4+ of 6 AI engines (50-business observational audit) | [1slake5](https://www.reddit.com/r/localseo/comments/1slake5/) | **Practitioner-reported** observational audit; small n; methodology not independently reviewed |
| 15+ consistent listings claimed ~3× more likely to be recommended | same cluster / community commentary around 1slake5 | **Practitioner-reported**; correlation ≠ causation |
| GBP completeness 82→100% → calls 14→26/mo (~86%), visibility 40→70% in ~12 days | [1r23zji](https://www.reddit.com/r/localseo/comments/1r23zji/) | **Practitioner-reported**; community pushback: may be reclassification / seasonality, not pure completeness lift |
| Primary category swap: page 2 → top 3 in ~3 weeks | [1viqf8z](https://www.reddit.com/r/localseo/comments/1viqf8z/) | **Practitioner-reported** Maps/pack outcome |
| Delete ~40 bad citations: accuracy 74→98%, calls doubled ~6 weeks | [1v7s2ae](https://www.reddit.com/r/localseo/comments/1v7s2ae/) | **Practitioner-reported**; no controlled AI re-prompt evidence in thread |
| GBP opt ~70% calls ~2 months; AI mentions stayed flat (comment) | [1o08abp](https://www.reddit.com/r/localseo/comments/1o08abp/) | **Practitioner-reported**; useful as “Maps ≠ AI” anecdote |
| Reviews 11→34, page 2→pack, calls doubled ~6 weeks; attribution fight (~80% reviews?) | [1vn7vjv](https://www.reddit.com/r/localseo/comments/1vn7vjv/) | **Practitioner-reported**; heavy confounding |
| HVAC: review velocity as biggest lever | [1vtlpki](https://www.reddit.com/r/localseo/comments/1vtlpki/) | **Practitioner-reported** vertical anecdote |
| Citations-only → top 8–15, not top 3 | [1m4woes](https://www.reddit.com/r/localseo/comments/1m4woes/) | **Practitioner-reported**; citations help floor, not crown |
| Yext “data hostage” / listings vanish when stop paying; Synup cancel fee ~$1,980; Uberall enterprise-only | community threads (vendor pricing folklore) | **Practitioner-reported** / vendor-commercial; verify contracts case-by-case |
| DIY gets RESULT credit; paid pipes for scale past ~30 locations / 100+ | community consensus | Product implication: ownership & tickets > hostage syndication |
| AI evidence thin vs Maps call evidence dense; ListingsAPI: no win case study | community meta | Aligns with BrightLocal / Insites gaps below |

**Pain for this proposal:** the North Star fails when (a) the shop is missing from AI answers, (b) the shop is named but facts are wrong, or (c) the owner has no accuracy-aware audit + ticket path across Lane A umbrellas and Lane B majors.

### What works (with evidence)

#### Maps / GBP / reviews (dense call evidence — mostly practitioner + expert surveys)

| Lever | Evidence | Label | URL / cite |
| --- | --- | --- | --- |
| Primary GBP category, proximity, keywords in GBP title, address in city, open-now | Whitespark LSRF 2026 “pack kings” (survey posted ~Nov 6, 2025) | **Vendor / expert survey** (47 experts, 187 factors) | [whitespark.ca/local-search-ranking-factors](https://whitespark.ca/local-search-ranking-factors/) |
| GBP completeness / category / reviews → pack & calls | Multiple r/localseo case posts (table above) | **Practitioner-reported** | reddit threads linked above |
| Review volume & recency | Insites dual-surfaced avg **133.4** Google reviews vs **10.7** for neither; Whitespark: review recency underrated | **Primary study** + **expert survey** | [Insites benchmark](https://insites.com/blog/the-ai-visibility-benchmark-how-10000-local-businesses-actually-perform-in-chatgpt-and-perplexity/); Whitespark LSRF 2026 |
| Dedicated service pages, best-of lists, industry domains, unstructured citation *quality*, review-site authority | Whitespark LSRF 2026 AI-factor discussion | **Vendor / expert survey** | same LSRF page |
| DIY ownership of GBP + majors | Community: DIY gets RESULT credit | **Practitioner-reported** | r/localseo meta |

#### AI visibility (thinner causal evidence; stronger descriptive studies)

| Finding | Evidence | Label | URL |
| --- | --- | --- | --- |
| AIO ~68% vs local packs ~39% on Whitespark local-business query set (540 queries; May 12, 2025) | Whitespark / Miriam Ellis prevalence study | **Primary study** (vendor-affiliated) | [whitespark.ca case study](https://whitespark.ca/blog/case-study-the-prevalence-of-ai-overviews-in-local-search/) |
| Maps mention 66% vs AI surfaces 32–38%; ChatGPT ∩ Google AIs ~19–20%; persistence ~50%; top citations GBP 28.6%, Yelp 9.5%, Facebook 2.2% | BrightLocal Local AI Visibility Study, Sep 16, 2026 (~200k prompts) | **Primary study** (vendor) | [brightlocal.com/research/local-ai-visibility-study](https://www.brightlocal.com/research/local-ai-visibility-study/) |
| Consumer AI local recs 6%→45% YoY; 23% used AI in last local search; of AI users ~18% contact direct, ~75% check Google/Maps | BrightLocal LCRS 2026 / related AI-trust cuts | **Primary study** (survey) | [LCRS](https://www.brightlocal.com/research/local-consumer-review-survey/); [LCRS AI trust](https://www.brightlocal.com/research/lcrs-ai-trust/) |
| ChatGPT recommends **1.2%** of brand locations vs **35.9%** in 3-Pack (~30× selective); ~45% retail overlap traditional vs AI | SOCi LVI 2026 (~350k locations / 2,751 brands) | **Primary study** (vendor) | [soci.ai news](https://www.soci.ai/news/in-ai-driven-discovery-few-brands-are-chosen-most-disappear/); [LVI PDF](https://golocal.soci.ai/rs/355-TUF-572/images/SOCi%202026%20LVI.pdf) |
| GBP detail match ChatGPT ~55.58% / Perplexity ~48.05%; never recommended unbranded 40.88% / 68.79%; “corroboration, not optimisation”; schema/llms.txt barely correlate | Insites 10k US locals (Jul 2026) | **Primary study** (vendor platform) | [Insites benchmark](https://insites.com/blog/the-ai-visibility-benchmark-how-10000-local-businesses-actually-perform-in-chatgpt-and-perplexity/); [schema piece](https://insites.com/blog/schema-wont-save-you-what-10000-local-businesses-taught-us-about-ai-visibility/) |

**What “works” for North Star Presence + Recommendability (synthesis, not a causal claim):**

- **GBP excellence remains mandatory** — proof hub, Maps call volume, and the strongest public corroboration signal assistants echo.
- **Consistent, high-quality presence on majors + umbrellas** supports corroboration (Insites framing) more than vanity citation spam.
- **Review velocity and volume** correlate with both pack and dual-AI surfacing; still confound-heavy for AI alone.
- **Measuring AI separately from Maps** is itself a “what works” practice (BrightLocal Maps 66% vs AI 32–38%; SOCi selectivity).

### What doesn't / waste

| Anti-pattern | Why it fails / is waste | Label |
| --- | --- | --- |
| Recurring listing fees for ≤~10 locations | Whitespark commentary: prefer one-time / DIY at small scale | **Vendor / practitioner** |
| Mass low-DA citation blasts | Expert consensus: noise; citations-only stalls at ~top 8–15 | **Expert survey + practitioner** |
| Selling citation-cleanup-only as pack/AI ROI | **No clean published cleanup-only → +X% pack/calls study** (Whitespark: no clean citation-cleanup-only ROI study; BrightLocal: no published aggregator-correction → +X% pack/calls study) | Gap — do not sell |
| Yext-like ongoing pipe as the product | “Data hostage” / listings vanish when stop paying; Synup cancel-fee folklore (~$1,980); Uberall enterprise-only | **Practitioner-reported** commercial risk |
| Assuming Uberall breadth / older directory studies = cleanup ROI | Older breadth studies ≠ correction ROI | **Vendor historical** — not cleanup ROI |
| Treating Localeze Completeness Score as an AI audit | Completeness = hub fill-in (Primary / Discovery / Connect / Hours), not web AI Presence/Recommendability | Product fact (Lane A) |
| Schema / llms.txt as the AI lever | Insites: barely correlate with recommendation | **Primary study** |
| Citations-only strategy for top 3 | Practitioner: top 8–15 ceiling without GBP/reviews/on-page | **Practitioner-reported** |
| ListingsAPI as proven win path | Community: no win case study found | **Practitioner-reported** / **UNVERIFIED** absence |
| Equating OpenAI↔Foursquare partnership with ranking lift | Partnership / Places adjacency ≠ proven ranking lever | Architecture caveat |
| Foursquare “60–70% of ChatGPT local” | Often phrased as “reports suggest” | **UNVERIFIED** — do not treat as fact |

BrightLocal framing remains useful commercially: **Google first, LLMs second** — not because AI doesn’t matter (LCRS 6%→45%), but because Maps still mentions tracked businesses more often and most AI users still verify on Google/Maps.

### AI visibility vs classic local SEO framing

| Dimension | Classic local SEO (Maps / pack) | AI visibility (assistants / AIO) |
| --- | --- | --- |
| Unit of success | Rank in 3-Pack / Maps; calls from GBP | Named in answer; facts correct; re-prompt persistence |
| Selectivity | 3-Pack ~35.9% brand appearance (SOCi) | ChatGPT ~1.2% recommended (SOCi) — ~30× tighter |
| Overlap | Pack ranking helps but does not guarantee AI mention | BrightLocal: Maps 66% vs AI 32–38%; intersection ChatGPT∩Google AIs ~19–20% |
| Stability | Rankings move; calls are measurable | Persistence ~50% on re-prompt (BrightLocal) |
| Top “citations” / sources | GBP, site, reviews, citations | GBP still top among directories (~28.6%), but business sites dominate unique domains; Yelp/Facebook trail |
| Expert factor mix (Whitespark LSRF 2026) | Pack kings: primary category, proximity, GBP title keywords, in-city address, open-now | AI lean: best-of lists, service pages, industry domains, unstructured citation *quality*, review-site authority |
| Consumer behavior | Call / direction intent | LCRS: AI recs mainstream (45%); many still verify on Google/Maps |
| Accuracy failure mode | Wrong hours on GBP hurt conversion | Assistants misstate NAP ~half the time even when “aware” (Insites) |
| Product scoreboard | Rank + calls | **Presence + Recommendability** (this proposal’s North Star) |

**Framing rule for GEOLOCAL:** classic local SEO is necessary infrastructure and the densest call evidence; AI visibility is a **separate, more selective** surface that needs its own audit (mentioned vs recommended vs **misstated**). Do not collapse them into one vanity “AI SEO” score.

### Gaps and caveats

1. **Causal AI ROI from cleanup is unpublished.** No BrightLocal / Whitespark / SOCi paper cleanly shows “aggregator correction alone → +X% AI mentions or pack.” Do not invent one.
2. **r/localseo numbers are observational.** Threads like 1slake5 (73% invisible…), 1r23zji (completeness→calls), etc. are **practitioner-reported**; n is small; confounders (season, category change, ads) are real. Community already flags reclassification risk on completeness wins.
3. **Vendor studies have incentives.** BrightLocal, Whitespark, SOCi, Insites sell adjacent products. Still useful as **primary study** descriptive baselines — cite methodology, don’t treat as gospel.
4. **Foursquare 60–70% of ChatGPT local = UNVERIFIED** when phrased as “reports suggest.” OpenAI partnership ≠ ranking lever. Managed Users timing remains an open question in the proposal.
5. **Localeze Lane A mechanics:** ~$99/year per location paywall; **no free suggest-edit**; Completeness Score = hub sections (Primary / Discovery / Connect / Hours), not a web AI audit; monthly partners often **30–90 days** — set owner expectations.
6. **Uberall / Yext / Synup commercial caveats** are practitioner folklore until contract-checked; still inform “don’t build a hostage pipe.”
7. **ListingsAPI:** no community win case study located — treat as non-proof, not proof of failure.
8. **Insites “corroboration, not optimisation”** undercuts schema/llms.txt-first GEO pitches; aligns with ticketed accuracy across real claim surfaces.
9. **SOCi sample is multi-location brands** (~350k locations). SMB single-shop dynamics may differ; still the clearest public selectivity ratio (~30×).
10. **BrightLocal citation mix** (GBP 28.6% / Yelp 9.5% / Facebook 2.2%) is study-specific; website citations dominate unique domains — own site still matters.

### Implications for this proposal (North Star Presence + Recommendability; Lane A/B)

Architecture already in this file / concept SoT:

- **Two-lane:** Lane A umbrellas (Data Axle, Localeze, Foursquare) + Lane B majors (GBP, Apple Business Connect, Bing Places, Yelp).
- **North Star:** Presence + Recommendability; [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) intent = discover + recommend by AI.

**Build toward:**

1. **Accuracy-aware AI audit** — fixed buyer prompts; score Presence (found on claim surfaces / stomped copies) and Recommendability (named when it matters; facts match claimed truth). Prefer mentioned vs recommended vs **misstated** over vanity mention counts.
2. **One-click tickets for Lane A and Lane B** — field / wrong / right / GBP-or-claimed-truth as proof. Umbrella-only leaves Core majors wrong; majors-only leaves Layer-1 feeds stomping the claim.
3. **GBP excellence still required** — densest call evidence; strongest corroboration signal; proof hub for tickets. Do not market AI audit as a GBP substitute.
4. **Don’t sell fake cleanup ROI** — no published aggregator-correction → +X% pack/calls or AI-mention study. Sell measurable re-prompt improvement and accurate tickets, not guaranteed pack jumps.
5. **Measure AI separately from Maps** — BrightLocal / SOCi / Insites all show divergence. Product dashboards must not blend pack rank into an “AI score.”
6. **DIY result credit ⇒ product posture** — make tickets and ownership easy; avoid Yext-like hostage pipes where Presence evaporates when the subscription stops. Paid pipes remain relevant for scale (~30+ / 100+ locations per practitioner consensus), as optional scale lanes — not the identity of the product.
7. **Localeze / Foursquare honesty** — charge/time expectations (Localeze paywall, 30–90 day partners); Foursquare Placemaker suggest-edit is real; OpenAI adjacency is not a ranking claim; Managed Users later.
8. **Evidence hygiene in GTM** — label practitioner vs primary study; flag UNVERIFIED (e.g. Foursquare 60–70%); keep this brief as the evidence appendix so the commercial ladder stays North-Star-tight.

**Success check (unchanged intent):** re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up,” not “we submitted 400 citations.”