AskRank — AI-recommendation scout for home-service pros

1. One-liner

Finds why ChatGPT names your competitor in your trade and town, then hands you the fixes to win.

2. Trend signal — why now?

In roughly twelve months, “who’s the best plumber near me?” moved from Google to the chat box. Consumer use of AI to find local businesses jumped from 6% to 45% in early 2026 — a 7.5× swing in a single year. Nearly half of ChatGPT conversations (49%) are people asking for recommendations and advice on who to hire. And AI-referred leads convert far harder: one dataset put ChatGPT referral conversion at 15.9% vs 1.76% for Google organic — roughly a 9× gap, because the person shows up pre-qualified by the model’s recommendation.

Here’s the gut-punch for local owners: only 1.2% of 350,000+ analyzed locations were recommended by ChatGPT, versus 35.9% appearing in Google’s local 3-pack. And the businesses that win Google’s map pack are not the ones AI names — only ~45% overlap. A 600-query study across ChatGPT, Claude, Gemini and Perplexity in 10 Florida metros found 95.4% of plumbers appeared on just one platform, and zero appeared on all four. Winning Google no longer wins the AI answer.

Meanwhile the money confirms the shift: the AI-visibility / GEO tooling market raised $300M+ between mid-2025 and spring 2026, and Profound raised a $96M Series C at a $1B valuation (Feb 2026) serving 700+ enterprise customers. But that capital is aimed at Fortune-500 brand teams and ecommerce — not the guy running three trucks.

Provenance:

• Signal 1 (demand): Consumer AI-for-local-search use jumped 6%→45% in early 2026; only 1.2% of local businesses get recommended by ChatGPT; ChatGPT referral converts at 15.9% vs 1.76% Google — https://pushleads.com/ai-search-visibility-for-local-service-businesses-why-your-google-page-one-ranki/ — 2026-07-17
• Signal 2 (feasibility): LLMs now cross-reference GBP/Yelp/BBB/Bing/Foursquare + reviews (Perplexity uses reviews in 100% of responses); schema markup + answer-first pages are the documented fix, all buildable on off-the-shelf LLM APIs — https://www.baadigi.com/blog/hvac-contractor-chatgpt-visibility — 2026-07-17
• Signal 3 (economic): GEO tooling raised $300M+ (mid-2025–spring 2026); Profound $96M Series C at $1B valuation Feb 2026; AEO retainers land at $2K–$10K/mo — https://www.surmado.com/blog/best-ai-visibility-tools-2026 — 2026-07-17 Category: Platform shift

3. The opportunity

A platform shift just re-drew the map of local discovery, and the tools built for the old map don’t cover the new one.

Two camps exist, and there’s a canyon between them:

• Old local-SEO / listings tools (Yext, BrightLocal, Moz Local, Whitespark). Built around Google rankings and NAP consistency. They’ll sync your name/address/phone across directories, but they were designed to win the map pack, not to make a language model trust and name you. They don’t test what the AIs actually say, and they don’t generate the answer-first, schema-marked content the models cite.
• New GEO / AI-visibility tools (Profound $1B, Peec, Otterly, Visiblie, Evertune). These monitor — they tell you whether ChatGPT mentions your brand, at $29–$199/mo up to enterprise. But they’re built for national brands and ecommerce, they mostly stop at “here’s your share of voice,” and they leave the fixing to a $2K–$10K/mo agency retainer.

Nobody owns the middle: the owner-operator home-service business that needs to (a) know why the AIs name a competitor and (b) get the specific fixes shipped — without a $3K/mo retainer. The fix is unusually mechanical for this segment: it’s almost always cross-source data inconsistency (profile accuracy sits at ~68% on AI platforms), missing schema (80% of local service sites have none), and vague “family-owned since 1998” pages instead of answer-first content (“we replace water heaters in Salinas, most jobs same-day, $800–$1,400”). That’s diagnosable and largely auto-fixable. AskRank is the scout and the fix-list — pointed at one trade, one town, one owner.

4. Target market

• Primary customer: Owner-operator or office manager of a US home/local-service business — HVAC, plumbing, roofing, electrical, garage-door, pest, and adjacent high-ticket local services (med-spa, dental, cosmetic, family law). 1–3 locations, $500K–$5M revenue, 3–30 staff. The person who answers the phone or signs the marketing check.
• Why they buy (their words): “A customer told me they found my competitor on ChatGPT and I have no idea why it’s not me.” “I pay $1,500 a month for SEO and I still don’t come up when someone asks the AI.” They feel the lead loss but can’t see the cause or the fix.
• Rough TAM reasoning: The US has hundreds of thousands of these businesses across the core trades — ~120K HVAC firms, ~130K plumbing firms, ~110K electrical, plus roofing, med-spa (~10K+), dental (~180K practices), and law. Conservatively 1M+ US local-service businesses with the revenue and the pain to pay $79–$249/mo. Capturing 3,000–5,000 of them is a $3–5M ARR business.
• Why now for them: The 6%→45% adoption jump means the lead loss became material this year, not someday. Younger homeowners (35 and under) increasingly start in ChatGPT/Perplexity. Owners are hearing “found you on the AI” (or not) at the counter for the first time, and their existing SEO agency has no answer.

5. Product sketch (MVP)

• The AI report card. Enter trade + service area. AskRank runs a battery of real buyer prompts (“best HVAC company in [town],” “who repairs tankless water heaters near [zip],” “emergency plumber [town] reviews”) across ChatGPT, Gemini and Perplexity, multiple times each (answers are non-deterministic — one run is a coin flip), and scores how often you’re named, cited, ranked, or absent — with your top 3 named competitors beside you.
• The “why not you” diagnosis. For each miss, the specific cause: your GBP address/phone disagrees with Yelp/BBB/Bing; you have no review-schema or LocalBusiness schema; your service page is atmosphere, not answers; a competitor is referenced across more cited sources.
• The fix list, in priority order. Not a report — a checklist of exact corrections: the four listings to reconcile and to what values, the schema block to paste, and the three answer-first page rewrites that will make you citable.
• Auto-generated answer content. For the pages that are hurting you, AskRank drafts the answer-first rewrite (service, area, price band, timeline, FAQs) formatted the way models cite — copy-paste or push to the site.
• Competitor teardown. Shows exactly which sources are making a named competitor the AI’s pick, so the owner knows what to match.
• Re-scan + drift alerts. Weekly re-runs; email/SMS when you fall out of an answer you used to win, or when a competitor overtakes you.
• Multi-location + agency view (expansion). Roll up several locations, or let a local marketing agency run it across their book of clients.

6. AI angle — what’s load-bearing

Two places, and remove either and the product collapses:

1. The measurement itself is AI. The core asset is programmatically interrogating ChatGPT/Gemini/Perplexity with hundreds of buyer-intent prompts, at repetition, and parsing free-text answers to detect whether the business was named, in what position, with what sentiment, versus which competitors. There is no API that returns “are you recommended” — you have to ask the models and read the answers. That’s an LLM-orchestration and NLP-extraction job end to end.
2. The fix is AI. Generating answer-first service pages and schema that match how a given model cites sources — and rewriting an owner’s vague copy into the factual, question-answering form models prefer — is exactly the generation task LLMs are good at. A dumb template can’t do it per-trade, per-town, per-service.

Strip the AI out and you’re left with a static checklist nobody updates. The whole loop — ask, read, diagnose, generate — is AI.

7. Localization angle (if any)

N/A for v1 — this is a US-first play by design. The wedge is hyper-local (US trades, US listing ecosystem: GBP, Yelp, BBB, Angi, Bing Places), and the buyer, currency, and prompt phrasing are all US. The natural expansion is geographic (UK/Canada/Australia local-service markets, same playbook, different directory set) rather than language localization. Note the deliberate portfolio choice: this is a US/HomeServices play precisely because the recent catalog skews India/SEA regulatory — no forced localization angle here.

8. Business model — path to $1M–$5M ARR

• Pricing: Self-serve tiers. Starter $79/mo (one location, one trade, weekly scans, fix list). Pro $149/mo (more prompts/competitors, auto-generated content, drift alerts). Multi-location / Agency $249–$499/mo (roll-up, white-label report, several locations or clients). One-time $99 AI Audit as a paid front door for the skeptical.
• ACV: Blended ~$1,500/yr (heavy weight on Starter/Pro).
• Rough math to $1M ARR: ~560 customers × $149/mo × 12 = $1.0M. Very reachable against a 1M-business TAM and a live pain.
• Rough math to $5M ARR: ~2,800 paying customers at a $149 blend, or a smaller base tilted toward Agency/multi-location seats. Requires a working agency channel (one agency reselling to 30 clients = 30 seats) and low churn — the drift-alert + monthly-fix loop is the retention engine.
• Expansion path: Location count → agency seats → adjacent verticals (med-spa, dental, legal at higher price tolerance) → a “done-for-you” tier where AskRank pushes the listing fixes and publishes the content, not just recommends them. The audit is the wedge; the recurring monitor-and-fix loop is the ARR.

9. Go-to-market wedge — first 100 customers

• Free “AI Report Card” as the hook. Build a public tool: owner types trade + town, gets a live, teardown-honest scorecard showing they’re absent from ChatGPT while three named competitors aren’t. The gap is visceral and self-evidently urgent — that’s the conversion moment. Gate the fixes behind signup.
• Cold outreach with the report attached. Scrape a trade directory (e.g. a state HVAC/plumbing licensing list or an Angi/BBB category), run each business’s report card in bulk, and email the owner their own scorecard: “Here’s what ChatGPT says when someone asks for an [trade] in [town] — you’re not in it, these three are. Here’s the 20-minute fix.” A personalized “you’re losing leads and here’s proof” beats any generic pitch. Expect low-single-digit reply, high intent when it lands.
• Sell through local marketing agencies. The 48% of agencies charging $1,500–$5,000/mo SEO retainers have no AI-answer product and their clients are asking. White-label AskRank as their “AI visibility” line item — one agency = 10–30 seats. This is the ARR multiplier.
• Trade-community distribution. Trade Facebook groups, r/hvacadvice / r/Plumbing / r/roofing owner threads, and trade-show booths — post the free report card tool where owners already gather and complain about lead gen.
• Vertical case studies. Land 5 HVAC wins with before/after “named by ChatGPT” screenshots; that becomes the entire HVAC pitch, then repeat per trade.

10. Build complexity — justification

Medium. Everything is off-the-shelf: LLM APIs for querying and generation, public listing/GBP/review data, a standard web app, and a scan scheduler. The custom work is the prompt battery per trade, reliable parsing of non-deterministic free-text answers into a stable score, and the listing-reconciliation logic across sources. No proprietary data, no model training, no hardware. A technical founder plus a part-time content/SEO hand ships a credible v1 in ~10–14 weeks; the free report-card tool alone is a ~4-week build and doubles as the lead magnet.

11. Gating checklist

Gate	Pass?	Note
Legal in target market	✅	Querying public AI tools and public listings; generating content for a client’s own site. No scraping of gated data required.
Ethical — no harm / dark patterns	✅	Helps small businesses be accurately represented; honest scorecards, not manipulation. Must avoid review-fraud tactics (see risk flags).
Market exists (evidence above)	✅	45% adoption, 1.2% recommendation rate, $300M+ GEO funding, live owner complaints.
1–5 person team can build this	✅	1–2 people to v1; API-orchestration + web app.
Launchable with <$50K / ₹40L	✅	Main cost is LLM API spend for scans; manageable with usage caps and paid tiers gating volume.

12. Feasibility score

Axis	Weight	Score	Notes
Problem intensity	20	16/20	Real, money-losing, felt now — “my competitor got the ChatGPT lead.” Just shy of hair-on-fire because many owners still under-attribute lost leads to AI.
Demand evidence	15	13/15	Multiple hard signals: 6%→45% adoption, 1.2% recommendation rate, 9× conversion, $300M+ funding, agency retainers. A skeptic nods.
Build feasibility	15	12/15	All off-the-shelf; the parsing-non-deterministic-answers-into-a-stable-score problem is the only real engineering discipline needed.
Distribution clarity	15	12/15	Free report card + scraped-list cold outreach + agency white-label is concrete and cheap. Conversion math still unproven.
Revenue mechanics	15	12/15	Pricing benchmarked below GEO tools and far below agency retainers; SMB churn is the open question.
Time to first revenue	10	7/10	Free-audit-to-paid funnel can convert in weeks, but building trust in a new category takes some runway.
Defensibility	10	4/10	Copyable — well-funded GEO players could move down-market. Moat is vertical focus, trade-specific content library, and agency lock-in, not tech.
Total	100	76/100

13. Qualitative modifiers

Founder-fit tags

technical-heavy · content-heavy — LLM orchestration and free-text parsing plus a per-trade answer-content library. A solo technical founder with local-SEO literacy (or a marketer co-founder) is ideal.

Key assumptions to validate (3–5)

1. Assumption: Owners will pay $79–$149/mo once shown they’re absent from AI answers while competitors aren’t. How to test: Run 50 free report cards via cold email, measure signup → paid conversion on the fix list.
2. Assumption: The fixes actually move the needle — reconciling listings + schema + answer content demonstrably increases how often the AIs name the business within 4–8 weeks. How to test: Instrument 10 pilot customers, re-scan weekly, track name-rate change.
3. Assumption: Local marketing agencies will white-label this as a retainer line item. How to test: Pitch 15 agencies; target 3 signed pilots reselling to their book.
4. Assumption: Non-deterministic AI answers can be turned into a stable, trustworthy score owners believe. How to test: Run the same prompt battery 10× across a week; measure score variance; if it swings wildly, the scorecard loses credibility.

Risk flags

1. Platform dependency: The product depends on continued programmatic access to ChatGPT/Gemini/Perplexity answers; TOS changes or rate limits could raise cost or restrict querying. Mitigate by diversifying engines and framing value around the fix, not just the scan.
2. Incumbent encroachment: A funded GEO player (Profound-scale) or Yext/BrightLocal could launch a local-service tier and out-resource you. The defense is speed, vertical depth, and agency relationships — not technology.
3. Attribution / efficacy risk: If the recommended fixes don’t reliably increase AI recommendations (models are opaque and change often), churn spikes and the value story breaks. This is the single biggest kill risk.
4. Ethics guardrail: The category is adjacent to review-gaming and fake-Reddit-mention tactics (documented as cheap and effective). AskRank must stay on the legitimate side — accurate listings and real content — or it torches trust and invites platform bans.

14. Structured verdict

Score:                  76/100
Verdict:                GO
Confidence:             Medium
Best-fit builder:       Technical founder with local-SEO/marketing literacy (or a marketer co-founder)
Time to revenue:        6–10 weeks (free audit → paid fix list)
Capital to launch:      $8–15K (mostly LLM API spend + a landing/tool build)
Top 3 assumptions to validate first:
  1. Owners convert free report card → paid — run 50 personalized scorecards via cold email, measure paid signup
  2. The fixes measurably raise AI name-rate in 4–8 weeks — instrument 10 pilots, re-scan weekly
  3. Agencies will white-label it — pitch 15, sign 3 pilots reselling to their client book
Kill criteria:
  - Abandon if <5% of 50 personalized free-audit recipients convert to paid within 60 days
  - Abandon if pilot customers show no measurable AI name-rate lift after applying fixes for 8 weeks
  - Abandon if a well-funded GEO incumbent ships a local-service tier at comparable price before your v1 traction

15. Next step — 1-week validation sprint

• Day 1–2: Hand-build the report card for one trade in three towns — run a 30-prompt battery across ChatGPT/Gemini/Perplexity for 15 real HVAC businesses, and produce a one-page “here’s who the AIs name and why it’s not you” for each. No product, just the output.
• Day 3–4: Cold-email those 15 owners their own report card with a Loom walking through the fix list, priced at a $99 audit or a $79/mo plan. Also DM 5 local marketing agencies the concept + a sample white-label report.
• Day 5: Decide go / no-go on a falsifiable bar: at least 3 of 15 owners (20%) reply asking to buy the fix, OR at least 1 agency commits to a paid pilot. Below that, the pain isn’t urgent enough to sell against — revisit pricing or vertical before building.