Investor Presentation · February 2026

Level Up
App Factory

AI-Native Consumer Apps at Scale

levelupbasket.com
Seed Round · $500K
Part 1 · The App Factory
The Opportunity

Consumer apps are broken.
AI fixes the economics.

99% of consumer apps fail. Most are static tools in a world that now expects AI-native experiences.

99%
of consumer apps fail within 2 years
5 days
our build time per app (vs. 3–6 months industry)
$2K
ad test budget to validate demand
~10%
graduate rate — but winners scale fast

AI changes three things simultaneously: build cost drops 10x, test cost drops 10x, and product quality jumps — because every app ships with an embedded AI assistant that makes it genuinely better than static alternatives.

Part 1 · The App Factory
Our Model

The Factory Pipeline

Research, build, test, and scale — in weeks, not quarters.

RESEARCH AI deep research
→
SPEC AI-native design
→
BUILD 5 days
→
LAUNCH Same-day deploy
→
TEST $2K ad spend
→
GO / KILL Day 14 decision
8
apps launched per month (1/dev/week)
~10%
graduate to scaling phase
$41K
total monthly burn rate
Part 1 · The App Factory
Deep Research Engine

AI-Powered Market Intelligence

5-phase automated pipeline turns raw markets into actionable app specs.

🔍
Wide Scan
12 parallel Gemini agents
🎯
Gap Detection
Find unserved needs
🔬
Deep Dives
Reddit, forums, reviews
📊
Synthesis
Score & rank
🌐
URL Resolution
Verify live sources

Output Per Niche

  • 50–80K words of structured analysis
  • 2–5 minutes generation time
  • Real Google Trends + Keyword Planner data
  • Competitor teardowns with pricing
  • User voice extraction (exact quotes)

3 Attack Strategies

Underserved

No AI-native solution exists yet

Predator

Beat a weak incumbent with AI UX

Provisioning

Automate an expert service at 1/10 cost

80 niches already researched and scored. Top 20 in active pipeline.

Part 1 · The App Factory
The Build Machine

Production-Grade in 5 Days

Built by parallel AI agents, not by hand. Every app ships with an embedded AI assistant.

Development Engine
Claude Opus 4.6
Anthropic's most capable coding model
Architecture
Team Agent
6–7 parallel agents per build
First app (BookScout) was built by a swarm of 6–7 AI agents working in parallel — frontend, backend, AI pipeline, infrastructure, testing — coordinated by a lead agent. This is why 5-day builds are possible.
Next.js 15
App Router + PWA
Cloud SQL
PostgreSQL 15
Prisma
ORM + Migrations
Firebase Auth
Auth + SSO
Vertex AI
Gemini 2.5 Flash/Pro
Stripe
Billing + Webhooks
Cloud Run
Serverless Deploy
Serwist PWA
Offline + Install

Every App Includes

  • Embedded AI assistant (streaming responses)
  • Stripe subscription (3-tier pricing)
  • PWA with offline support + install prompts
  • Firebase auth (Google, email, anonymous)
  • Mobile-first responsive design

Automated Provisioning

  • Single command: GCP project + DB + DNS + SSL
  • GitHub repo + CI/CD pipeline created
  • Firebase project configured
  • Stripe products + webhooks wired
  • Cold-start elimination (min-instances=1)
Part 1 · The App Factory
Growth Engine

AI-Generated Growth at Scale

$2K test budget per app. ROAS is the only metric — kill below 70%, scale above 100%.

Ad Testing Protocol

$1,200
Meta Ads (FB + IG)
$800
Google Ads (Search + Display)
  • AI-generated ad creative variants
  • Auto-kill underperformers at Day 4 / 7 / 10
  • Facebook Pixel + Conversions API (CAPI)
  • GA4 event tracking for full funnel

Real-Time Market Data

  • Google Trends API for demand signals
  • Keyword Planner for CPC + volume
  • Competitor ad library monitoring
  • A/B test: hooks, benefits, urgency angles

Optimization Cadence

Day 4: Kill worst ad sets · Day 7: First go/kill signal · Day 10: Reallocate budget · Day 14: Final decision

Part 1 · The App Factory
Decision Framework

One Metric: Return on Ad Spend

Production and hosting costs are negligible. The only question is: does the marketing math work?

Kill
<70%
ROAS too low
No path to profitability — kill and move on
→
Optimize
70%+
Signal detected
Add budget, improve creatives, push toward 100%
→
Scale
100%+
Profitable unit economics
Start scaling — every ad dollar returns more than $1
→
Growth Portfolio
150%+
Proven winner
User feedback → app improvements → scale further

Why ROAS, Not CPA

  • Production & hosting cost ≈ $0 per user
  • Team is the only fixed cost — already covered
  • ROAS directly answers: "Is this a business?"
  • No need for complex LTV projections early on

AI-Driven Decisions

  • AI monitors ROAS in real-time across all apps
  • Auto-kills underperforming ad sets at Day 4/7/10
  • AI generates creative variants to push ROAS up
  • Framework is fully automated — humans set thresholds
Part 1 · The App Factory
Unit Economics

Near-Zero Variable Costs. Only Marketing Matters.

Team is the only fixed cost. Production, hosting, and AI costs are negligible per user. So the entire business question reduces to one thing: ROAS.

Why Costs Don't Matter

~$0.02
AI cost per interaction (Gemini)
~$35–65
Hosting per app per month (Cloud Run)
85–90%
Gross margin on every subscription

With margins this high, the only cost that determines success or failure is customer acquisition — which is exactly what ROAS measures.

The ROAS Equation

ROAS = Revenue from Ads / Ad Spend
<70%
Kill
70%
Optimize
100%+
Scale

At $4.99/mo with 8-month avg lifetime:
LTV = $39.92 — so even a 50% ROAS means $20 revenue per $40 ad spend, with massive headroom to optimize.

Fixed Costs Covered Separately

Team ($22K/mo) is funded by the raise, not by app revenue. Every dollar of ROAS above 100% is pure profit to reinvest.

Part 1 · The App Factory
Founder & Team

Engineer Who Scaled to 150M Users

Eugene Lisovskiy
Founder & CEO · Physics Engineer · 3x Founder
linkedin.com/in/lisovskiy →
150M
users at MAPS.ME (CEO)
80x
user growth at LitRes (CMO)
$15M
ARR at LitRes (from $600K)

Why He'll Win This

  • Engineer first. Physics background, still codes and prototypes — built an AI coach demo in 2 hours
  • Scaled before. MAPS.ME to 150M users, LitRes from 300K to 23M users and $15M ARR
  • Marketing-native. Was a CPO before the term existed — fused engineering and growth at LitRes in 2011
  • AI-native builder. Develops with Claude Opus 4.6 + Team Agent — 6–7 parallel AI agents per build
  • Mission-driven. Chose impact over easy money at every turn — open maps, basketball over gaming, AI tools over AI hype

The Team — $22K/mo payroll

Founder — $12K

Strategy, product, growth decisions, AI development

AI Builder 1 — $4K

1 app/week. Full-stack + AI integration. Owns build pipeline.

AI Builder 2 — $3K

1 app/week. Full-stack + AI integration. Owns infrastructure.

Growth Operator — $3K

3–5 niches researched/week. Ad campaigns launched same-day.

Monthly Burn: $41K

Payroll — $22K (54%)
Ad Testing — $16K (39%)
Tools & Infra — $3K (7%)

Payroll: Founder $12K · Dev $4K · Dev $3K · Growth $3K | Ad testing: 8 apps × $2K/app

Part 1 · The App Factory
Portfolio Strategy

ROAS-Driven Automated Pipeline

Every stage is AI-automated. Human decisions set thresholds — AI executes the rest.

8 Apps Launched / Month (1 per dev per week)
$2K Ad Test · AI Monitors ROAS
ROAS ≥ 70% → Optimize Creatives, Add Budget
ROAS ≥ 100% → Start Scaling
Growth Portfolio → User Feedback → Improve → Scale More

Automated Improvement Loop

  • AI generates + tests ad creative variants
  • User feedback collected → AI improves app
  • Better app → better conversion → higher ROAS
  • Higher ROAS → more budget → more users
  • Entire loop runs with minimal human input

Portfolio Compounds

  • Each graduated app is a self-sustaining revenue engine
  • Diversification across niches reduces risk
  • Shared infrastructure = near-zero marginal cost
  • Learnings from one app accelerate the next
  • Revenue grows as portfolio grows
Part 2 · BookScout Deep Dive
First App · Live & Deployed

BookScout

Shazam for Bookshelves

Snap a photo of any bookshelf → AI reads every spine → instant identification, prices, ratings, and a witty shelf roast.

Try It Live → bookscout.levelupbasket.com
Live on Cloud Run
ROAS Score: 8.1 / 10
#1 of 81 Targets Evaluated
Part 2 · BookScout Deep Dive
Market Opportunity

The Book Discovery Revolution

42.9M
Physical book browsers (US TAM)
2.3M
Professional book resellers
13.4M
Potential subscribers (SAM)

Year 1 Target

$6M ARR
50K subscribers at blended ARPU

Tailwinds

  • "Bookshelf Wealth" trend on social media
  • BookTok: 250B+ views driving physical book sales
  • 55% showrooming rate (browse in-store, buy online)
  • Used book market growing 8% YoY
Part 2 · BookScout Deep Dive
The Problem

Two Personas, Two Pain Points

Manual processes in a world that should be instant.

📚

Casual Reader

  • Manual cataloging takes hours
  • "What should I read next?" — discovery gap
  • "Tsundoku" guilt — unread book pile grows
  • Sees an interesting shelf — can't ID books fast enough
"I spent 3 hours manually adding my books to Goodreads. There has to be a better way." — r/books
💰

Book Reseller

  • 15+ minutes to scan 50 books one-by-one
  • Social stigma scanning in stores
  • Current tools: $44/mo (ScoutIQ) barcode-only
  • Miss valuable books hiding in plain sight
"I look like a weirdo scanning every single barcode at Goodwill. Wish I could just snap the whole shelf." — r/FlippingBooks
Part 2 · BookScout Deep Dive
Competitive Landscape

No AI-Native Solution Exists

Every competitor is either barcode-only, stagnant, or generic.

Feature ScoutIQ Goodreads StoryGraph Google Lens BookScout
Full Shelf Scan (AI) ✕ ✕ ✕ ✕ ✓
Spine Text Reading ✕ barcode only ✕ ✕ ~ generic ✓ book-optimized
Reseller Pricing ✓ ✕ ✕ ✕ ✓
AI Recommendations ✕ ✕ stagnant ✓ basic ✕ ✓ taste profile
Shelf Roast / Fun ✕ ✕ ✕ ✕ ✓ viral mechanic
AI Chat Assistant ✕ ✕ ✕ ✕ ✓
Price $44/mo Free $4.99/mo Free $4.99/mo

BookScout is the only product that reads entire bookshelves via AI, provides reseller pricing, and includes a conversational AI assistant — at 1/9th the price of the leading reseller tool.

Part 2 · BookScout Deep Dive
Product & AI Pipeline

4-Stage AI Vision Pipeline

From photo to full book metadata in under 12 seconds.

📸
Image Capture
PWA camera input
👁️
Spine Detection
Gemini 2.5 Vision
📖
Text Reading
OCR + AI extraction
🔗
Book ID
Google Books API
✨
Enrichment
Prices, ratings, recs
$0.005
cost per scan
>85%
identification accuracy
<12s
full pipeline latency

Core Features

  • Scan: Snap any bookshelf, AI IDs every book
  • Library: Auto-cataloged with metadata
  • Roast: AI-generated witty shelf commentary

Premium Features

  • Recommendations: Taste-profile based
  • Price Lookup: Reseller arbitrage data
  • Chat with Scout: AI book advisor
Part 2 · BookScout Deep Dive
Business Model

3-Tier Subscription Pricing

85–90% gross margin. Break-even at 84 Flipper subscribers.

Free
$0
forever
3 scans per month
Basic book identification
Library up to 50 books
No reseller pricing
No AI chat
Pro
$4.99
/ month
Unlimited scans
Full book metadata
Unlimited library
AI recommendations
Shelf roasts
Flipper
$29.99
/ month
Everything in Pro
Reseller pricing (live)
Profit calculator
Arbitrage alerts
Chat with Scout AI

Break-Even Analysis

84 Flipper subs = all infra costs covered

Or ~500 Pro subs for equivalent revenue

LTV / CAC Projections

4–7x
Casual readers
12–50x
Resellers (high LTV)
Part 2 · BookScout Deep Dive
Traction & Status

Built, Deployed, Ready to Test

Full product live on production infrastructure. Ad testing imminent.

Development Milestones

  • ✓ Full AI vision pipeline operational
  • ✓ Deployed to Cloud Run (production)
  • ✓ PWA with camera integration
  • ✓ Stripe billing (3 tiers) wired
  • ✓ Firebase Auth configured
  • ✓ AI assistant (Chat with Scout) built
  • ✓ Shelf roast feature live
  • ✓ Library + enrichment pipeline

Quality Metrics

27 / 27
Fix tests passing
36 / 37
Smoke tests passing

ROAS Score

8.1 / 10

#1 of 81 targets evaluated by deep research

Part 2 · BookScout Deep Dive
The Ask

$500K Seed Round

~12 months runway at $41K/month burn.
Path to profitability by Month 6–8.

Use of Funds

Team — $264K
Ads — $192K
Infra & Tools — $36K
Payroll: Founder $12K · Dev $4K · Dev $3K · Growth $3K
12-Month Ad Budget ($16K/mo × 12)
Infrastructure & Tools ($3K/mo)

Path to Profitability

Month 3
First graduated apps generating revenue
Month 6
Portfolio revenue covers ad spend
Month 8
Revenue offsets full burn rate

Eugene Lisovskiy

eugene@levelupbasket.com

levelupbasket.com

Research Methodology

Deep Research Methodology

Every idea goes through 5 phases of AI-powered research before we decide to build

425 Ideas Researched
600+ Avg Searches Per Idea
43K+ Avg Words Per Idea
9 AI Agents Per Phase
Research Pipeline
Each idea flows through five sequential phases — from raw data to final verdict
425 Niche Ideas
▼
Phase 0
📡
Data Enrichment
Pull Google Trends, keyword volumes, CPC benchmarks, and competitive signals for each idea
▼
Phase 1
🔍
Wide Scan
9 specialized AI agents launch in parallel — each performs 20+ web searches independently
Round 1 — Cast a Wide Net
9 agents run in parallel. Each explores its topic broadly with 20+ fresh Google searches.
🗣️
User Voices
20+ searches
🏿
Product Landscape
20+ searches
🔑
Keywords
20+ searches
📢
Ad Strategy
20+ searches
🔧
Build & Tech
20+ searches
💰
Monetiz.
20+ searches
⚠️
Stack Pitfalls
20+ searches
📊
Financial Intel
20+ searches
🏢
Competitive Intel
20+ searches
9 Reports Collected
Round 2 — Go Deep on Gaps
Each agent sees its own Round 1 report, then runs 15+ new searches to fill gaps. Zero repetition — every word must be net-new. Targets: missed competitors, exact pricing, dead startups, ad library intel, funding data.
🗣️
User Voices
20+ searches
🏿
Product Landscape
20+ searches
🔑
Keywords
20+ searches
📢
Ad Strategy
20+ searches
🔧
Build & Tech
20+ searches
💰
Monetiz.
20+ searches
⚠️
Stack Pitfalls
20+ searches
📊
Financial Intel
20+ searches
🏢
Competitive Intel
20+ searches
▼
18 Independent Reports — 360+ Searches Total
▼
Phase 2
🔎
Gap Detection
1 auditor agent reads all 18 reports — finds contradictions, missing data, unanswered questions
▼
8–15 Follow-up Tasks Identified
▼
Phase 3
🎯
Deep Dives
8–15 targeted agents investigate each gap with 20+ focused searches
🔬
Deep Dive 1
🔬
Deep Dive 2
🔬
Deep Dive 3
···
🔬
Deep Dive N
▼
8–15 Targeted Reports — 300+ Additional Searches
▼
Phase 4
⚖️
Final Synthesis
Synthesizer scores idea 1–10 across weighted dimensions, produces GTM blueprint
BUILD
CONDITIONAL
KILL
Three Research Strategies
Each idea is researched through one of three strategic lenses, with agents tailored to that lens
🦈

Predator

Target incumbents where AI gives us a decisive cost or quality advantage. Find their weaknesses, exploit them.
101 ideas researched
  • Customer Complaints
  • Incumbent Analysis
  • Demand Validation
  • Switching Barriers
  • Clone Feasibility
  • Price Disruption
  • Stack Pitfalls
  • Financial Intelligence
  • Competitive Intel
💎

Underserved

Find problems with no good existing solutions. Serve users that incumbents ignore or underserve.
163 ideas researched
  • User Voices
  • Product Landscape
  • Keywords & Search Demand
  • Ad Strategy
  • Build & Tech
  • Monetization
  • Stack Pitfalls
  • Financial Intelligence
  • Competitive Intel
🌱

Provisioning

Spot emerging trends before solutions exist. Build the definitive tool before anyone else arrives.
161 ideas researched
  • Behavior Signals
  • Adjacent Solutions
  • Trend Demand
  • Market Entry
  • Build Feasibility
  • Market Economics
  • Stack Pitfalls
  • Financial Intelligence
  • Competitive Intel
Phase 1 Agents (Underserved Example)
Each agent is a specialist — here's what the 9 agents investigate for underserved niches
🗣️

User Voices

Scrapes Reddit, forums, and reviews for real user frustrations, workarounds, and language. Captures exact quotes for ad copy.

20+ searches × 2 rounds
🏿

Product Landscape

Maps every existing solution — apps, tools, templates. Analyzes pricing, review counts, ratings, and feature gaps.

20+ searches × 2 rounds
🔑

Keywords & Search Demand

Analyzes search volumes, CPCs, keyword difficulty, and long-tail opportunities. Estimates addressable search market.

20+ searches × 2 rounds
📢

Ad Strategy

Evaluates ad viability across Meta and Google. Estimates CAC, models creative angles, analyzes competitor ad patterns.

20+ searches × 2 rounds
🔧

Build & Tech

Assesses technical feasibility with our stack. Identifies API dependencies, AI model requirements, and potential blockers.

20+ searches × 2 rounds
💰

Monetization

Models pricing strategies, willingness-to-pay, LTV projections, and conversion benchmarks for the niche.

20+ searches × 2 rounds
⚠️

Stack Pitfalls

Checks for known technical gotchas — API rate limits, platform restrictions, legal constraints, data availability issues.

20+ searches × 2 rounds
📊

Financial Intelligence

Researches comparable product revenues via Google Search. Builds ARR estimates (conservative/base/optimistic) with CAC, LTV, and pricing models.

20+ searches × 2 rounds
🏢

Competitive Intel

Profiles competitor business health: team sizes, funding, ad spend, revenue estimates, AI-native readiness. Assesses market concentration and moat strength.

20+ searches × 2 rounds
ROAS-First Scoring Framework
Ideas are scored on weighted dimensions that prioritize ad economics and business sustainability
Ad Viability
20%
Can we profitably acquire users via Meta/Google ads?
Monetization
20%
Clear path to revenue — willingness to pay, LTV potential
Competition Gap
15%
Is the market open enough for a new AI-native entrant?
Competitive Intel
15%
How well-resourced and entrenched are existing competitors?
Build Feasibility
15%
Can we ship an MVP in 3-5 days with our stack?
Market Size
10%
Enough demand to sustain $10K+ MRR at scale?
Aha Speed
5%
How fast does the user feel magic after first interaction?
What Each Idea Produces
By the time an idea gets a verdict, we have a comprehensive research package
18
Phase 1 Reports
9 agents × 2 rounds
1
Gap Analysis
with 8-15 follow-up tasks
8-15
Deep Dive Reports
targeted investigations
1
Final Synthesis
verdict + GTM blueprint