Three practices, one perspective.
Every engagement starts with a clear scope: what decision are you making, by when, and what would change your mind. Aureak brings frameworks and data. You keep the decision.
AI infrastructure advisory
For small businesses, startup founders, mid-market CTOs, and Fortune 500 CIOs.
Most AI infrastructure decisions are made under pressure with incomplete information. Vendors push their solutions. Teams argue over benchmarks. The right answer depends on scale, workload profile, compliance posture, and team capacity — none of which fit in a demo.
From business inputs (users, task complexity, content type) to GPU counts and total facility power. Concrete numbers, not vendor hand-waving.
At each stage of scale, one path dominates. We map your workload against API-only, managed reserved, self-hosted (leased), and build-your-own economics — and identify the crossover points.
Hyperscalers vs Neoclouds. Specialized providers. Model providers vs infrastructure providers. Which combination fits your workload, compliance, and cost of capital.
Structuring 1-3 year reserved capacity to lock in discounts without over-committing to a specific technology path.
Focused engagements typically run 2–6 weeks. Ongoing advisory available on retainer.
Investor diligence
For venture capital, private equity, sovereign wealth, infrastructure funds, corporate venture, and chip makers.
AI infrastructure is capital-intensive, technically deep, and moving fast. Traditional due diligence frameworks miss the operator-level questions that determine long-term economics: cost of capital, customer concentration, structural cost advantage, and workload flexibility.
Neoclouds, model providers, AI infrastructure companies. Assessment of technical differentiation, unit economics, and defensibility against hyperscaler competition.
Landscape mapping across GPU providers, networking, storage, cooling, and platform. Where structural advantage exists and where it doesn’t.
Ongoing operator-level support for portfolio companies making infrastructure decisions. Vendor negotiations, sizing, build vs buy.
Second opinion on capital allocation across the AI infrastructure stack. Where the compute cycle is going, and what that means for existing positions.
Diligence engagements typically run 1–4 weeks. Ongoing advisory available on retainer.
Executive coaching and interim leadership
For enterprises scaling AI infrastructure teams, and funds needing operator-level oversight of portfolio companies.
Scaling an AI infrastructure practice is fundamentally different from scaling a traditional IT function. Different vendor relationships. Different capital cycles. Different talent market. Leaders need someone who has been through it.
One-on-one advisory for VPs of Infrastructure, Heads of Platform, and CTOs navigating AI-driven scale.
3–9 months embedded in a company or portfolio company as infrastructure or platform lead during a transition. Build the team, set the strategy, hand over to permanent leadership.
How to structure an AI infrastructure team at 10, 50, 200 people. What roles to hire first, and when.
Coaching typically monthly. Interim leadership: 3–9 month engagements.
Not sure which fits your situation?
The first call is 30 minutes and costs nothing. Bring your question.
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