AskProof

1. One-liner

Shows a local business every AI question where a rival gets named instead — and fixes the profile gap behind it.

2. Trend signal — why now?

The discovery layer for local services flipped in about twelve months, and almost nobody selling to local businesses has repriced for it.

• Google shipped “Ask Maps” on 12 March 2026 (iOS + Android, US and India first, wider rollout through Q3 2026). It’s a Gemini-powered conversational layer inside Google Maps: you type “is there a plumber near me who can come out today and takes card,” and it answers with a shortlist. It ranks on attribute match — categories, amenities, hours, photos, and the text content of reviews — not on the backlink-and-proximity machinery the entire local SEO industry was built to game.
• Google Business Profile impressions per location fell 53.8% in six months (Birdeye, State of Google Business Profile 2026). Customer actions — calls, clicks, direction requests — fell only ~5%. Read that carefully: the funnel didn’t shrink, the browsing stage got deleted. An AI now pre-qualifies the shortlist before a human ever sees a profile.
• Consumer use of AI tools to find local services went from 6% to 45% in one year (BrightLocal, Local Consumer Review Survey 2026).
• SOCi analyzed 350,000+ business locations: ChatGPT names 1.2% of them. Google’s 3-pack surfaced businesses roughly 36% of the time. The winner’s circle shrank by a factor of thirty.
• And the punchline: there’s only ~45% overlap between the businesses that win the Google map pack and the businesses AI actually recommends. More than half of everyone currently paying for local SEO is winning a race that no longer pays out.

Meanwhile the tooling hasn’t moved. BrightLocal starts at $39/mo, Localo ~$70/mo per location — both still sell geo-grid pin rank tracking, a metric that measures a surface losing 53.8% of its impressions. The people who have adapted are the GEO/AEO agencies, and they charge $1,500–$12,000/month (Digital Elevator, Fuel Online pricing guides, 2026). A two-van plumbing outfit is not paying $1,500/mo. That gap — enterprise-priced answer, SMB-sized wallet — is the whole business.

Provenance:

• Signal 1 (demand): GBP impressions −53.8% in six months while customer actions fell only ~5%; consumer AI use for local search 6% → 45% — https://www.forbes.com/councils/forbesbusinesscouncil/2026/05/14/your-google-business-profile-is-now-an-ai-feed/ (Birdeye State of GBP 2026; BrightLocal Local Consumer Review Survey 2026) — 2026-05-14
• Signal 2 (feasibility): Google ships “Ask Maps,” a Gemini-powered conversational local search ranking on attribute-and-review match, 12 March 2026 — https://techcrunch.com/2026/03/12/google-maps-is-getting-an-ai-ask-maps-feature-and-upgraded-immersive-navigation/ — 2026-03-12
• Signal 3 (economic): SOCi study of 350,000+ locations — ChatGPT recommends 1.2% vs Google 3-pack ~36%; GEO/AEO agencies charging $1,500–$12,000/mo to fix it — https://core6.marketing/blog/why-chatgpt-recommends-your-competitor-not-you/ and https://thedigitalelevator.com/blog/aeo-and-geo-pricing-guide/ — 2026-06-25 Category: Platform shift

3. The opportunity

Local SEO is a mature, ~$5B/yr industry whose core product measures the wrong thing.

The incumbent tools (BrightLocal, Local Viking, Nightwatch, Grid My Business) all sell the same artifact: a geo-grid heatmap showing your pin’s rank at 49 lat/long points around your shop. That artifact made sense when a human scrolled a map and picked from what they saw. It is close to meaningless when Gemini reads your reviews, decides you don’t do emergency callouts, and never puts you in the answer at all.

Nobody is selling the obvious replacement at a price a local business will pay: a tool that asks the AI the questions your customers actually ask, records whether you got named, tells you who got named instead, and — this is the part that matters — tells you the specific thing in your profile or your review corpus that caused the miss.

The reason it’s fixable and not just a scoreboard: Ask Maps and the LLMs rank on stated attributes. If Gemini never recommends you for “open Sunday,” it’s usually because your hours say closed, or your reviews never once contain the phrase. That’s a concrete, closable gap — a service attribute you never ticked, a Q&A you never wrote, a review-request script that never asks the customer to mention what you fixed. Those are 15-minute jobs. The hard part is knowing which 15-minute job to do, and that’s exactly what an LLM harness can compute and a human cannot.

Incumbent weakness in one line: BrightLocal will happily tell you you’re #1 in a race that only 1.2% of businesses now finish.

4. Target market

• Primary customer: Owner-operator local service businesses with 1–4 locations and $300K–$3M revenue, in high-intent, high-ticket, urgent-need categories — plumbers, HVAC, electricians, garage doors, roofers, dentists, med spas, auto repair, law and accounting practices. US first (Ask Maps launched US + India), then UK/AU/CA. The buyer is the owner or the one marketing person, and they already spend money on this problem — they’re paying an agency $500–$2,000/mo or a tool $39–$99/mo today.
• Why they buy: They can see the numbers. Their GBP dashboard shows impressions off a cliff. Their agency’s report still says “you rank #2 in the grid.” Nobody can explain the contradiction, and the honest answer — “an AI is deciding, and it doesn’t like your profile” — comes with no fix attached. AskProof is the first thing that shows them the actual answer text where a competitor got named and they didn’t. That screenshot sells itself.
• Rough TAM reasoning: ~1.1M SMB locations in the US already buy some form of local search service (the BrightLocal/Yext/Birdeye/agency customer base). I don’t need TAM math — I need 1,700 of them at $99/mo to clear $2M ARR. That’s 0.15% of the buyers who already pay for this category.
• Why now for them: Ask Maps is four months old and rolling out internationally through Q3 2026. Their leads are dropping right now and the diagnosis doesn’t exist yet. Twelve months from now BrightLocal ships this as a feature checkbox — the window is the window.

5. Product sketch (MVP)

• Prompt bank per vertical. 40–60 real customer questions per category (“emergency plumber open now near me,” “dentist that takes Delta Dental and sees kids,” “HVAC who works Sundays in [suburb]”), built from the way people actually phrase things — not keywords.
• Weekly answer sweep. Runs the bank against Gemini/Ask Maps, ChatGPT, Perplexity, and Google AI Overviews, from the right geography. Records the verbatim answer, who got named, in what order.
• The Miss Report. The headline artifact: “You were named in 6 of 52 questions. Here are the 46 you lost, the competitor who won each, and the exact sentence in their profile or reviews that won it.”
• Gap → task. Each miss resolves to one concrete action: tick the “emergency service” attribute, add three Q&As, publish a Sunday-hours post, add “gluten-free” to the menu description. Ranked by how many misses each single action would close.
• Review-language coach. The highest-leverage and least-obvious feature. Reviews are now ranking text, not just stars. AskProof generates the ask — “text this to Mrs. Patel: ‘glad we got the boiler going same-day — if you leave a review, mentioning it was an emergency weekend call really helps other people find us’” — so the review corpus grows the vocabulary the AI is looking for.
• Competitor named-rate. Side-by-side: you 12%, the rival across town 61%. Nothing motivates an owner-operator like that number.
• One-page monthly PDF an agency can white-label and hand to a client.

6. AI angle — what’s load-bearing

Rip out the AI and there is no product. The AI is the market: the thing being measured is an LLM’s recommendation behavior, which only exists because LLMs became the local discovery layer. And the diagnosis — reading a competitor’s review corpus and profile and identifying which stated attribute caused the win — is a semantic comparison task that a human would need forty minutes per query to do, and an LLM does for a fraction of a cent.

Cost sanity: 50 prompts × 4 engines × weekly = 200 calls/customer/week ≈ 10K calls/customer/year. At current inference prices that’s a couple of dollars a year per customer in raw model cost, plus scraped-answer parsing. Gross margin is not the risk here.

7. Localization angle (if any)

Global play, US-first, but with a real second wedge: Ask Maps launched in the US and India simultaneously. India has millions of local service businesses on GBP, a WhatsApp-native owner, and near-zero penetration of $39/mo Western local-SEO tools. A ₹999/mo WhatsApp-delivered version of the Miss Report — “this week Gemini recommended 3 dentists in Indiranagar, you weren’t one, here’s the one thing to fix” — is a distinct business with a distribution channel the US version doesn’t have. I’d run US for revenue and India as the second bet once the prompt bank generalizes.

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

• Pricing: $79/mo single location (Solo), $149/mo up to 3 locations + competitor tracking (Pro), $399/mo agency seat covering 10 client locations with white-label reports. Priced deliberately above BrightLocal ($39) and far below the GEO agencies ($1,500+) — I want to be the obvious “I can just buy this” number.
• ACV: ~$1,300 blended (mix of Solo, Pro and agency seats).
• $1M ARR: ~640 direct customers at $130/mo blended. Or ~210 agency seats at $399/mo. Realistically a mix: 400 direct + 100 agency seats ≈ $1.1M.
• $5M ARR: requires the agency channel to carry it — ~700 agencies × $499/mo avg (larger client rosters) + 1,500 direct. Needs international (UK/AU/CA/India) and 2–3 more verticals in the prompt bank. Achievable but it’s a year-3 shape, not year-2.
• Expansion path: locations → competitor slots → engines tracked → done-for-you profile edits (managed tier at $499/mo, where the margin actually lives) → review-request automation (adjacent to Birdeye’s core, and a fair upsell).

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

This is the strongest part of the idea, because the product generates its own cold-outreach asset.

1. The unsolicited Miss Report. Pick a city and a vertical — say, 300 HVAC companies in Phoenix. Run the prompt bank against each before contacting them. Email the owner one line and one screenshot: “I asked Gemini ‘best HVAC for emergency AC repair in Phoenix’ 40 different ways. You were named twice. [Competitor] was named 27 times. Here’s why.” That is a personalized, undeniable, zero-marginal-cost cold email, and the AI does the personalization. 300 emails/city, expect 8–12% reply on a claim that specific.
2. Agencies as the wholesale channel. There are thousands of small local-SEO agencies whose flagship deliverable (the grid heatmap) is quietly becoming indefensible and whose clients are asking hard questions about the impressions drop. Sell them the report as the thing that saves the retainer. One agency seat = 10 locations. Target the BrightLocal/Local Viking user community, r/SEO, the Local Search Forum, and the LocalU / Whitespark orbit.
3. The public leaderboard as content. Publish “AI Recommendation Rates by City and Vertical” — Phoenix HVAC, Austin dentists, Manchester plumbers. Every business named or not-named has a reason to click. This is the SEO play that actually works because the data doesn’t exist anywhere else, and the local search press (Search Engine Land, Search Engine Roundtable, BrightLocal’s own blog) covers original local data reliably.
4. Franchise and buying-group side door. Franchisors with 50–300 locations feel this at portfolio scale and have a marketing budget line. One franchise = 100 locations = $10K+/mo. Slower sale, but the agency tier is already the right shape for it.

10. Build complexity — justification

Low. There is no novel engineering here. It’s a scheduled job that runs prompts against four LLM/search APIs from geo-appropriate egress, parses which business names appear, diffs the winner’s public profile against yours, and renders a report. The two real pieces of work are (a) getting reliable, geo-located answers out of Ask Maps and AI Overviews, which are UI surfaces and not clean APIs — expect scraping infrastructure with proxies, and expect it to break; and (b) building a genuinely good prompt bank per vertical, which is domain work, not engineering. A competent pair ships a credible v1 in 8–10 weeks.

11. Gating checklist

Gate	Pass?	Note
Legal in target market	✅	Querying public search surfaces and reading public profiles. Scraping SERPs is a well-trodden grey zone the entire rank-tracking industry (BrightLocal, Semrush, Ahrefs) already operates in — but see risk flags.
Ethical — no harm / dark patterns	✅	Helps a business accurately describe what it genuinely offers. The review-language coach must never fabricate a review or incentivize one — it only helps a customer articulate a real experience. That line is bright and must not be crossed.
Market exists (evidence above)	✅	People already pay $39–$12,000/mo to solve the adjacent problem.
1–5 person team can build this	✅	Two people, 8–10 weeks.
Launchable with <$50K / ₹40L	✅	Proxies, inference, and a landing page. Under $10K to first revenue.

12. Feasibility score

Axis	Weight	Score	Notes
Problem intensity	20	17/20	Leads are visibly falling now and nobody can explain why. That’s the definition of hair-on-fire. Docked 3 because customer actions only fell ~5% — some owners haven’t felt real revenue pain yet, they’ve only felt dashboard pain.
Demand evidence	15	14/15	Four independent named studies (Birdeye, BrightLocal, SOCi, Ahrefs) plus an existing market paying $39–$12,000/mo. A skeptic nods.
Build feasibility	15	13/15	Off-the-shelf everything, except the Ask Maps / AI Overview scraping, which is genuinely fiddly and will break.
Distribution clarity	15	12/15	The unsolicited Miss Report is a great cold asset and the agency channel is real. Docked because deliverability at cold-email scale is its own discipline and conversion is unproven.
Revenue mechanics	15	11/15	$79–$399 is well-benchmarked and 640 customers to $1M is achievable. Docked because local SMB SaaS churns hard — 3–5%/mo is normal in this category and I’m assuming better.
Time to first revenue	10	9/10	You can literally sell the report before the product exists. Pre-sell in week 2.
Defensibility	10	6/10	Twelve months out, the moat is the longitudinal dataset — nobody else will have a year of “who did the AI name, in which city, for which question.” At month 3 there is no moat at all, and BrightLocal shipping this as a feature is the obvious kill shot.
Total	100	76/100

13. Qualitative modifiers

Founder-fit tags

technical-heavy · content-heavy — you need someone who can keep scrapers alive against a hostile surface, and someone who can build genuinely good per-vertical prompt banks and write the data-driven content that fuels the leaderboard play.

Key assumptions to validate (3–5)

1. Assumption: Ask Maps and AI Overview answers are stable enough to track — that the same prompt from the same geography returns a consistent enough business set to constitute a metric, rather than dice. How to test: run 40 prompts × 3 geographies × 14 consecutive days and measure name-set variance. If the answer set is essentially random day to day, the product is a random-number generator and the whole idea dies here. Do this first — it’s the load-bearing assumption.
2. Assumption: owners will pay $79–$149/mo for a diagnosis, not just a dashboard. How to test: hand-generate 30 Miss Reports, email them cold, and try to close 5 at $99/mo before writing any product code.
3. Assumption: the recommended fixes actually move the named-rate. How to test: pick 10 pilot businesses, apply the fixes, and measure named-rate over 6 weeks against a matched control group that changes nothing. If the fixes don’t move the number, this is astrology with a nice PDF.
4. Assumption: small local-SEO agencies see this as a retainer-saver and not a threat. How to test: 20 discovery calls with agency owners; count how many ask “can I white-label this” versus “why would I show my client they’re losing.”

Risk flags

1. Platform dependency (severe). This entire product is downstream of Google and OpenAI surfaces that have no supported API for what you’re reading, and every incentive to block automated querying. Anti-bot escalation is not a hypothetical — it’s the operating cost of the business. If Google hard-blocks Ask Maps scraping, the product loses its headline engine overnight.
2. Incumbent fast-follow. BrightLocal, Birdeye and Semrush all have the distribution and the engineers to ship “AI visibility” as a feature line. Several already market a version of it. The bet is that a focused tool that produces a fix list beats a checkbox that produces a score, and that a year of longitudinal data beats a feature launch. That’s a real bet, not a certainty.
3. The efficacy risk. If Ask Maps ranking turns out to be dominated by things the owner cannot change — Google’s own entity graph, prominence, sheer review volume — then the “gap → task” promise is hollow and the product decays into a scoreboard nobody renews. Assumption 3 exists to find this out early and cheaply.
4. Metric-not-money. Impressions fell 53.8%; customer actions fell ~5%. If that gap holds, some owners will correctly conclude the sky isn’t falling and won’t buy. The counter is to sell to the categories where the AI shortlist genuinely gates high-ticket, urgent purchases — which is why the ICP is emergency trades and high-ACV professional services, not gift shops.

14. Structured verdict

Score:                  76/100
Verdict:                GO
Confidence:             Medium
Best-fit builder:       Technical founder who has kept scrapers alive before, paired with someone
                        who can write and sell to local business owners. Prior local-SEO or agency
                        exposure is worth more than it looks.
Time to revenue:        4–6 weeks (pre-sell the hand-made report before the product exists)
Capital to launch:      $8–12K (proxies, inference, landing page)
Top 3 assumptions to validate first:
  1. AI answer stability — 40 prompts × 3 geos × 14 days; is the named-set a metric or is it noise?
  2. Willingness to pay for a diagnosis — 30 hand-made Miss Reports, close 5 at $99/mo, no code written
  3. Fix efficacy — 10 pilots vs matched control, does named-rate actually move in 6 weeks
Kill criteria:
  - Abandon if day-to-day variance in the named business set exceeds ~40% for identical prompts —
    you cannot sell a metric that is dice
  - Abandon if the 10-pilot cohort shows no statistically meaningful named-rate lift over control
    after 6 weeks of applied fixes
  - Abandon if <3 of 30 hand-delivered Miss Reports convert to a paid $99/mo commitment
  - Abandon if Google hard-blocks automated Ask Maps querying and no viable proxy path survives

15. Next step — 1-week validation sprint

• Day 1–2: Build the prompt bank for one vertical in one city — 40 real customer questions for HVAC in Phoenix. Run it against Ask Maps, Gemini, ChatGPT and AI Overviews, twice a day, from Phoenix-egress IPs. Log every business named.
• Day 3–4: Compute variance. This is the gate. If the same question returns a wildly different business set morning and evening, stop — there’s no metric here and the rest of the week is wasted. If it’s stable, hand-build 30 Miss Reports for the 30 Phoenix HVAC companies who lost the most.
• Day 5: Cold-email all 30 with their own report and a $99/mo offer, priced as a founding rate. No product. No demo. Just the report and a Stripe link.
• Decide: go if (a) named-set variance is under ~40%, and (b) at least 3 of 30 owners put a card down on a report alone. Fewer than 3 and the diagnosis isn’t worth paying for — which is the only thing this business actually sells.