Krew Financial Model

• Live, gamified fitness classes streamed browser-based, no hardware or download required ("Peloton without the $3k bike") slide 02.
• Real-time motion-tracking AI scores participants and drives leaderboards visible to all during class slide 04.
• Pros get monetisation tools: per-class, packages, subscriptions, on-demand pay-per-view slide 04.
• Pro-facing features: individualized feedback, pose correction with annotation, CRM, automated reminders, notes per client, in-person class management slides 04, 16, 17.
• Proprietary P2P + edge computing architecture makes unit cost of delivering livestreaming + AI ~1/100th of cloud/CPaaS competitors (Krew TURN/signaling: $1,200/month vs. AWS Chime $90k+ for equivalent sessions) slide 12.

• Global remote health & wellness market: $11.0bn (2020) → $60.9bn (2026), 33% CAGR slide 06.
• Global fitness app market: $3.6bn (2020) → $12.0bn (2026), 22.3% CAGR slides 03, 06.
• 87% of gym-goers cancelling membership or adding online option post-COVID slide 03 (image).
• People working from home (UK): 5% before lockdown → 49% during lockdown; 80% "would like to" slide 03 (image).
• People working out daily (UK): 31m before lockdown → 31m during lockdown, +6m increase attributed despite no gyms slide 03 (image).
• Under-employed fitness professionals: 740,000 US / 57,000 UK slide 03.
• SOM framing: "Krew customers needed for $100m ARR" - figure redacted slide 06; addressable base is 35.4m Americans who watched more online exercise videos due to social distancing, out of 221.6m Americans who engage in physical activity slide 06.

• Business model is redacted in the deck slide 07.
• Revenue mechanism implied by context: transaction/take-rate on pro earnings (consistent with "we only make money when pros make money" statement slide 04).
• Monetisation tools for pros include: per-class payments, packages, subscriptions, on-demand pay-per-view slides 04, 08.
• Average consumer spend per month: redacted slide 06. Competitive benchmarks shown: ClassPass $80–$160/month, Peloton $150/month, Orangetheory $279/month slide 06.
• US gym-goers price distribution: 46% pay $0–$30/month, 32% pay $30–$50/month, 11% pay $50–$70/month slide 06 (image). Krew positioning is in the value tier relative to Peloton/Orangetheory.
• No monthly fee charged to pros slide 04.

All traction metrics are redacted in both the JSON text and the slide image slide 08:

• Number of pros on waitlist/directory: redacted
• Number of pros onboarded: redacted
• WoW customer retention %: redacted
• % of customers using Krew 1+ times/week: redacted
• % of customers using Krew 2+ times/week: redacted
• WAU targets at 6/12/18-month milestones: all redacted
• ARR targets at 12/18-month milestones: redacted

Note: deck is an investor deck from 2021 (cover slide 01); redactions appear to be investor-version redactions for public distribution.

• COGS advantage is a central thesis: Krew's P2P + edge approach delivers livestreaming at ~$1,200/month (TURN + signaling server) vs. ~$90k–$216k/month for CPaaS alternatives at equivalent scale (~30k sessions/month, 30 pax/session) slide 12.
• Gross margin: "incredible gross margins" claimed because no monthly pro fees and cost structure is dominated by very low video infrastructure cost slide 04. No specific gross margin % disclosed.
• AI delivery cost at scale: proprietary vs. AWS Studio Notebooks (ml.g4dn.2xlarge, Europe): $1,050,600–$2,112,000/month for equivalent AI; Krew cost: $0 (edge-computed) slide 12.

Competitive set (slide 07 - entrepreneur-enabler category):

• Playbook (similar: Sudor.fit): transaction fee 20%; 150 creators, "several thousand" waitlist slide 13.
• Exer.ai / Exerstudio.com: freemium; pre-launch at time of deck slide 13.
• Moxie.xyz: transaction fee 8–12%; 600 classes/week slide 13.
• TrueCoach (acquired): subscription B2B $19/$49/$99/month; 15,000 coaches slides 07, 13.

Broader category view (slide 15):

• ClassPass: credits-based subscription, offline managed marketplace.
• Peloton: hardware + subscription; live gamification only via $3k bike.
• Aaptiv: subscription, AI-led on-demand.
• Glofox: B2B supply-side infrastructure.

Moat claims:

• ~4 years of proprietary P2P/edge-computing R&D ahead of the market slide 05.
• Hardware-less real-time gamification is academically hard (P2P scalability requires PhD-level expertise); legacy players face inertia from cloud-first architectures slide 14.
• CTO (Dr. Yousef Amar): PhD distributed networks, Nokia Bell Labs, Imperial/Cambridge/Oxford slide 09.

Team slide 09:

• CEO: Jose Martin Quesada - McKinsey, Google, Avanade; London Business School / ESCP; consumer ML and digital growth.
• CTO: Dr. Yousef Amar - PhD distributed networks/edge computing; Nokia Bell Labs; Imperial/Cambridge/Oxford; patent in load balancing.
• Chief Research Officer: Dr. Marco Marchesi - Apple, Cambridge; AI in creative industries.
• Growth & product: Nuan Zhang (consultant + certified PT), London Business School.
• 3 developers (current).
• Planned hires at seed: Commercial Officer (ex-MD major in-person competition), Digital Marketing (ex-startup, 2m audience), additional developers.

Funding:

• Pre-seed raised: £0.08m slide 13.
• Raising: seed round, amount redacted; separately a "larger seed" scenario shown on roadmap slide 08.
• Use of funds: Not explicitly in deck. Implied uses: product stability (99%), AI v2, additional monetisation tools, CRM, referral tooling, headcount (commercial + digital marketing + developers) slide 08.

• Archetype + why: Two-sided marketplace / creator-economy SaaS hybrid. Revenue flows from a take-rate on gross booking value (pros earn → Krew takes %) and/or a platform subscription. This is structurally similar to a marketplace GMV model (gross bookings × take rate = revenue) with a SaaS-style pro-tier subscription layered on top. Given the early stage and fact that take-rate % is redacted, the model must flex on both the take-rate and average session/subscription price.

• Forecast horizon & granularity: Monthly for Years 1–2 (match roadmap milestones: today → 6m → 12m → 18m), quarterly for Years 3–5. Horizon: 5 years.

• Key drivers & assumptions:

Supply side (pros):

• Starting pro count: redacted; model with placeholder, e.g. low tens to low hundreds.
• Pro growth rate (MoM): 10–15% MoM early, tapering to 3–5% at scale; rationale: marketplace cold-start supply-first.
• Sessions per pro per month: 8–12 sessions/month; rationale: fitness instructors typically teach 2–3x/week.
• Average class size (participants): 10–20 pax; rationale: deck shows leaderboards with ~20 participants in product images.

Demand side (consumers):

• Conversion rate (consumer → paying after free class): 15–25%; rationale: freemium-to-paid benchmarks for fitness apps.
• Average consumer monthly spend: redacted; $15–$30/month (below ClassPass $80 floor; Krew positioned as accessible/value tier per slide 06 gym-fee distribution showing 78% pay <$50/month).
• Consumer retention (monthly): redacted; 70–80% monthly; rationale: fitness apps typically 60–80% monthly, Krew flagged "increasing retention" as KPI.

Revenue:

• Take rate on gross booking value: redacted; 15–25%; rationale: midpoint of comparable (Moxie 8–12%, Playbook 20%).
• Pro subscription (planned): $0 at launch (deck states no monthly fee), small B2B tier introduced in Year 2 for premium CRM/management tools.

Costs:

• Infrastructure COGS: ~$1,200/month for TURN/signaling server at current scale; scales roughly linearly with concurrent sessions.
• AI COGS: $0 at Krew (edge-computed); modest cloud cost for batch jobs.
• Staff: CEO, CTO, CRO, 3 devs now; seed hires add ~4–5 FTEs implied.
• S&M: heavy on content marketing / organic social (pro-led audience growth), paid CAC modest at seed.
• Gross margin target: 70–80% long-run; high because COGS are near-zero (edge-computing moat claim slide 04).

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: take rate 20%, avg consumer spend $20/month, pro MoM growth 10%, consumer monthly retention 75%.
• Bull: take rate 25%, avg consumer spend $25/month, faster pro growth 15% MoM, retention 82%.
• Bear: take rate 15%, avg consumer spend $15/month, pro growth 7% MoM, retention 65%, delayed AI monetisation.
• Key flex variable: avg consumer spend (most sensitive, entirely redacted); secondary: take rate and pro supply growth.

• Required sheets / outputs:

1. Assumptions - all drivers with/ tags, scenario toggles.
2. Pro Supply Build - pro cohort model (adds per month, churn, active pro count).
3. Consumer Demand Build - sessions per pro × avg class size × conversion rate → paying consumers; cohort retention waterfall.
4. Gross Bookings & Revenue - GBV = sessions × avg ticket; Revenue = GBV × take rate + subscription revenue.
5. P&L - Revenue → Gross Profit (infra + AI COGS) → EBITDA (headcount, S&M, G&A).
6. Headcount plan - current team + seed hires + scaling.
7. Cash / Runway - seed raise proceeds vs. monthly burn; months of runway.
8. KPI Dashboard - WAU, paying consumers, active pros, GMV, ARR, gross margin %, burn rate.
9. Scenario comparison - Base / Bull / Bear on ARR and runway.