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GlowUp

From ~10 Failed Apps to GlowUp — How a 24-Year-Old Solo Canadian Builder Used $0 in TikTok Ad Spend to Reach $800K in a Year and $1.2M ARR

GlowUp is a makeup app: upload a look you love, AI applies it to your own face and suggests the products to recreate it. After ~10 flops, Louis-David Paul-Hus launched it with $0 ad spend via TikTok and reached $800K in a year and $1.2M ARR.

Published: Jun 27, 2026Figures last checked: Aug 15, 20263 min readPrimary-source verified · 4
Monthly (est.)
$100k/mo
Time to grow
12 months
Users
500K
Launched
2024
Louis-David Paul-HusLouis-David Paul-Hus@LouisDavidPHFrom ~10 Failed Apps to GlowUp — How a 24-Year-Old Solo Canadian Builder Used $0 in TikTok Ad Spend to Reach $800K in a Year and $1.2M ARR

Key takeaways

  • Invert the order: stop building-then-distributing; pick the channel (TikTok) and the angle first, then build for it
  • Make the core an instant before/after (snap a look → AI applies it to your face) — designed for short-form video
  • Go all-in on $0 organic TikTok at first, learning which angle goes viral yourself at zero CAC

The pain point, and how they found it

“That makeup is cute — but how would it look on me?” Scrolling social, people see a look they love but stall: will it suit my face, and what do I buy to recreate it? That uncertainty is the core friction in cosmetics buying. GlowUp targets exactly that: upload one photo of a look you like, and AI previews it on *your* face and suggests the products to recreate it. The pain (will it suit me / what do I buy) is itself the product’s entry point.

Background & product

GlowUp lets you upload a photo of a makeup look you love; AI then previews that look on your own face and suggests the cosmetics you’d need to recreate it for real. The core is virtual try-on, makeup steps tailored to your features, and product recommendations based on skin tone. It’s operated by Viral Tech Inc., bills itself on the App Store as the “#1 AI makeup assistant,” and reports 500,000+ users.

It was built by solo Canadian developer Louis-David Paul-Hus (@LouisDavidPH). As he states on X, he’s a 24-year-old solopreneur, and before GlowUp he had shipped roughly ten apps that all failed. What changed wasn’t his tech but his *order of operations*: decide the distribution wedge (TikTok) first, then build the product — on the premise that ‘ship it to the store and they’ll find you’ no longer works.

Technically he built the app in the no-code/low-code tool FlutterFlow. He first tried RevenueCat for subscriptions but hit friction with FlutterFlow and migrated to Adapty. Rather than training a flashy in-house model, he ‘translated’ existing AI into a makeup experience and shipped fast with no-code to validate — a stack an indie builder can copy directly.

Growth was relentlessly ‘TikTok at $0 ad spend.’ The title of his own case-study video is the summary: “My mobile app made $800K in 365 days.” Multiply that by paywall optimization that plugged the leaks in monetization (below), and ARR climbed to $1.2M.

From the founder (primary source)

GlowUp growth channels and tech stack

How it was built

  1. Reverse the order — stop building then distributing, and decide the channel (TikTok) and the angle that lands before building the product
  2. Center the experience on a before/after that reads instantly: photograph a look you like, and AI recreates it on your own face
  3. Build the app in no-code/low-code FlutterFlow, translating existing AI into a makeup experience rather than training a model
  4. Try RevenueCat for billing, hit friction with FlutterFlow, and migrate to Adapty
  5. Measure paywall reach first — only 20% of users saw it — then A/B the placement to find the optimum (MRR $30K to roughly $100K)
  6. Don't over-supply price options: multiple plans lowered conversion, so converge on a single middle price that is neither too high nor too low
What AI did
No in-house model — existing AI is 'translated' into a makeup experience, with the app itself built fast in no-code (FlutterFlow) for validation

How it got users

  1. Go all-in on organic TikTok posts rather than paid ads at the start, learning at zero CAC which angles go viral
  2. Anchor on case-study videos featuring the founder — the title says it all: 'My mobile app made $800K in 365 days'
  3. When scaling distribution, partner with TikTok and Instagram creators on equity rather than cash
  4. Recruit a large pool of creators without any upfront ad spend, with 'the more it grows the more you earn' turning posts into genuine advocacy rather than one-off ads
Early ad spend
$0 — no paid ads; growth came from organic TikTok plus equity granted to creators

The repeatable playbook

  1. 1Invert the order: stop building-then-distributing; pick the channel (TikTok) and the angle first, then build for it
  2. 2Make the core an instant before/after (snap a look → AI applies it to your face) — designed for short-form video
  3. 3Go all-in on $0 organic TikTok at first, learning which angle goes viral yourself at zero CAC
  4. 4To scale, pay creators in equity, not cash — enlist many promoters with no upfront ad spend
  5. 5Measure paywall reach first (GlowUp started at ~20%), then A/B the placement (MRR $30K → ~$100K)
  6. 6Don’t offer too many price options; converge on a single middle price to cut hesitation and lift conversion
  7. 7Translate, don’t build: ship existing AI fast with no-code (FlutterFlow) and validate in small, fast loops

The hard parts

GlowUp wasn’t a first-try hit. By his own account, Louis shipped roughly ten apps before it and all failed. What made the difference wasn’t talent but abandoning the ‘build-then-distribute’ order — the pile of failures became the foundation for learning to design distribution first.

Updates since publication

  1. Aug 15, 2026

    The App Store listing (id6736916284) is now published by Zipo Apps Ltd. (display name ZipoApps, developer ID 1671444635, sellerUrl zipoapps.com) rather than Viral Tech Inc., the provider this case recorded as primary information. The app ID and release date (2024-10-15) are unchanged and the description still carries the original copy — “GLOWUP (previously GlamAI)” and “Join over 500,000 users!” — so this appears to be the same app record transferred to a different developer account. The ZipoApps developer page lists 200+ apps released between 2011 and 2024, i.e. a portfolio operator that acquires and runs apps. However, whether Louis-David Paul-Hus sold the app, when and on what terms, and whether he retains any involvement could not be confirmed from primary sources (his own site louisdavidpaulhus.com and x.com are unreachable under this environment’s egress restrictions). The sale is therefore not asserted; only the change of provider visible on the App Store is recorded as fact. Note that the revenue figure (MRR $100K) comes from the Adapty case study and reflects that reporting period, not the present state under the new provider. The app itself is live, last updated 2026-05-07 (v2.2.3), rated 4.42 from 13,456 ratings.

Deep dive

【Deep dive】The essence of GlowUp is *sequencing*. A builder with ~10 failures behind him won not by changing his tech but by changing the order in which he did things. Here is the reproducible breakdown.

He inverted the common thread of his failures: stop building-then-distributing. His prior ~10 apps followed the ‘build something good and they’ll find it’ order. But shipping to the store finds no one. Facing that plainly, Louis inverted the sequence: *first* pick the place you can distribute (TikTok) and the angle that lands, *then* build a product optimized for it. GlowUp’s core experience — snap a look you love, AI applies it to your own face — is itself designed to be instantly legible and imitable on TikTok. Same engineering ability, opposite outcome, just from reordering.

Distribution went all-in on $0 organic TikTok. He started not with paid ads but with organic TikTok posts. ‘Your ideal makeup, applied to your own face, just by taking a photo’ reads as an instant before/after — perfect for short-form video. At zero CAC he could read demand and personally learn *which angle goes viral*, then hand that winning formula to the next stage (creators). The title of his own video, “$800K in 365 days,” rests on this $0 acquisition base.

He paid creators in equity, not cash. To scale distribution, Louis is reported to have partnered with TikTok/Instagram creators using equity rather than cash payments. For a cash-poor solo founder this works on two levels: (1) he could enlist many creators without fronting ad spend, and (2) creators gained ‘the more it grows, the more I gain’ incentives, making their posts more committed than one-off paid ads. He built the early growth engine on *sharing the upside*, not on a marketing budget.

Plug the leaks: paywall optimization more than tripled MRR. Behind the growth, Louis tightened monetization. Per Adapty’s official case study, only ~20% of users were even *reaching* the paywall at first. After A/B testing placement and picking the optimum, MRR jumped from $30K to nearly $100K. No matter how hard you drive traffic, a paywall users never see earns nothing — the same inflow can yield a 3×+ difference purely from how it’s shown.

Pricing: ‘don’t offer too many choices’ was the answer. He also found that presenting multiple price plans up front actually *lowered* conversion (his hypothesis: too many options overwhelm users). After several experiments, converging on a single not-too-high, not-too-low middle price produced the best results. Pricing isn’t ‘charge more to earn more’ or ‘charge less to sell more’ — it’s a design problem of reducing hesitation. Stacked together, these optimizations grew ARR from $400K to $1.2M.

Tech: ‘translate,’ don’t ‘build’ — and iterate fast with no-code. GlowUp was built in FlutterFlow; subscriptions moved to Adapty after RevenueCat proved a poor fit. Instead of training a giant model, he translated existing AI into a makeup experience and shipped fast with no-code to validate. That ‘small, fast loops’ operating mode is also why ~10 failures became fuel. The indie moat isn’t frontier tech itself — it’s the product of (sequencing × $0 distribution × monetization tuning).

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