Turning compute requirements
into commercial opportunities.
AI teams need GPU capacity that fits their workload, timeline, and budget. My job has been to connect those requirements with a workable supply option.
The problem
A GPU model alone doesn't define a usable solution. Buyers also need the right configuration, location, networking, contract term, and deployment date.
My contribution
I built outbound campaigns, ran buyer discovery, sourced capacity from providers, and coordinated quotes, pricing, follow-up, and contract discussions.
The commercial challenge
Customer protections and supplier funding requirements can pull in different directions. A compelling price doesn't resolve every concern about delivery or upfront payment.
Inside the work: how I qualify an opportunity +
- Understand the workload. GPU model, quantity, topology, networking, and deployment requirements.
- Confirm the buying constraints. Budget, start date, location, term, and payment expectations.
- Match supply to the need. Compare provider capacity, pricing, and readiness before shaping a proposal.
- Move the conversation forward. Coordinate commercial questions and internal stakeholders.
What this taught me: Technical fit and commercial fit need to be qualified together. Interest in a proposal is a step in the process; it isn't a closed deal.
This case study describes my process. It does not claim closed revenue or disclose customer identities.
