Investor Dashboard
Key financial KPIs at a glance — % against revenues in QuickBooks-statement style.
Revenue Mix · % of Top Line
Cost Structure · % of Operating Cost
Use of Funds · % of $45M Raise
Problem & Solution
Pre-leased 12MW IT capacity for AI inference workloads in the GCC
The Problem
AI inference workloads are exploding in the GCC but Tier-3+ data center capacity is 14–22 months back-ordered. Hyperscalers preferentially serve global clients, leaving regional enterprises, fintechs, and sovereign AI programs without compute access. The supply gap is driving rack-rental prices 38% above 2023 levels.
Our Solution
A modular Tier-3+ data center platform delivering 1.5MW pods on a 9-month buildout cycle vs the 22-month industry norm. Pre-engineered liquid-cooling for AI inference, GPU-optimized power density (40kW/rack), and sovereign-cloud certification for regional government workloads.
Market Opportunity
$48B GCC DC TAM addressable today
GCC data center capacity $9B (2025) → $24B (2030) · 22% CAGR · AI inference 45% of new demand
Co-location MRR per rack ($1,200–$2,400 depending on power density), GPU bare-metal hosting ($4.50/GPU-hour), managed cloud services (40% gross margin attach), and long-term reserved capacity contracts (5–7 year terms).
Financial Statements · % vs Revenue
QuickBooks-style readout — every line shown as percentage of its parent total.
Revenue Mix
| Revenue Stream | % of Revenue | Share |
|---|---|---|
| Co-location Rack Rentals | 45.0% | 45% |
| GPU Bare-Metal Hosting | 28.0% | 28% |
| Managed Cloud Services | 15.0% | 15% |
| Reserved Capacity Contracts | 12.0% | 12% |
| Total Revenue | 100.0% | 100% |
Cost Structure
| Cost Line | % of Cost | Share |
|---|---|---|
| Power & Cooling | 30.0% | 30% |
| Facility Depreciation | 22.0% | 22% |
| Engineering & Ops Staff | 18.0% | 18% |
| Connectivity / Bandwidth | 12.0% | 12% |
| Security & Compliance | 10.0% | 10% |
| G&A | 8.0% | 8% |
| Total Operating Cost | 100.0% | 100% |
Use of Funds — $45M Raise
| Allocation | % of Raise | Share |
|---|---|---|
| Pod 1 & 2 Buildout (CapEx Equity) | 55.0% | 55% |
| Power Substation & Backup | 18.0% | 18% |
| Engineering & Ops Hires | 12.0% | 12% |
| Compliance & Tier-3 Cert | 8.0% | 8% |
| Working Capital | 7.0% | 7% |
| Total Use of Funds | 100.0% | 100% |
Valuation, Capital Structure & Forward View
An investment is a bet on the forward plan, so a trailing snapshot isn't enough. These are derived from this report's own ask and projections — not external estimates.
Capital Structure & Funding
A blended equity + debt structure — this round layers a credit facility (loan / project / trade / inventory finance) on top of the equity cheque. That puts leverage, debt service and lender covenants into the capital structure: drawdown conditions and coverage ratios are first-order diligence items, not footnotes.
How to read these
Rule of 40 sums forward revenue growth and EBITDA margin — ≥40 is healthy; below it flags growth bought at the cost of profit. Capital efficiency is Year-5 revenue per dollar raised. Entry multiple divides the disclosed cap / pre-money / asking price by Year-3 revenue, shown only where disclosed (n/d = not derivable). Verify against primary diligence.
Traction & Proof Points
- 12MW IT capacity pre-leased across 3 anchor customers (2 banks + 1 sovereign AI program)
- LOI from a top-3 GCC telco for 4MW reserved capacity over 7 years
- 9-month buildout cycle vs 22-month industry norm (modular advantage)
Moat & Exit Strategy
Defensible Moat
Pre-leased anchor tenants (3 customers covering 12MW) lock revenue before steel hits ground. Modular pod design compresses time-to-revenue by 13 months vs traditional builds — structural cost-of-capital advantage. Sovereign-cloud certification creates a regulated moat for government AI workloads.
Exit Path
Sale to a global data center platform (Equinix, Digital Realty, EdgeConneX) or regional infrastructure fund at 14–18x EBITDA on stabilized assets, or YieldCo IPO at $200M+ stabilized EBITDA within 6–8 years.
Key Risks
- Power-grid capacity constraints in target metros delaying buildout
- Hyperscaler entry compressing co-location pricing
- GPU supply allocation tied to NVIDIA / AMD distribution agreements
When the Thesis Breaks
Read this before trusting the forward numbers. The case rests on operating leverage — revenue growth converting into a holding-or-expanding EBITDA margin. The fastest way it breaks: a period where revenue grows but EBITDA falls (margin compression).
If any of the Key Risks above materialise, the forward projections in this report should be treated as suspended until the model is re-underwritten. The single most material trigger to watch: Power-grid capacity constraints in target metros delaying buildout.