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Building the Foundation · Group 1 of 4

The infrastructure decisions you make in the first six months are the ones you live with for the next three years.

Most AI startups do not design their infrastructure. They build it under pressure, one reasonable decision at a time, until the accumulated weight of those decisions becomes the thing slowing them down. Building the foundation properly from the start is not a luxury. It is the difference between infrastructure that scales with your product and infrastructure that fights it.

01

AI-Native Infrastructure

The problem

Most AI products are built on infrastructure that was never designed for AI workloads. Model works in notebook. In production it fails.

What we do

Design AI infrastructure from the ground up with your specific workloads, models, and growth trajectory. Not bolted on. Built in.

What you get

AI product that works in production as in demos. Predictable GPU costs. Vector databases that hold under peak load.

What's included

AI stack architectureGPU provisioningVector DB setupLLM pipeline designModel serving infrastructure
02

Cloud Architecture

The problem

Most startup cloud architectures were not designed. They grew. Costs climb without explanation. Security gaps nobody owns. All of it slows down the product eventually.

What we do

Design, migrate, build cloud infrastructure on AWS, Azure, GCP around how your product actually operates today and where it is going next.

What you get

Cloud foundation that scales with product. Costs you can explain. Security baseline that passes investor due diligence.

What's included

Cloud strategyMigration planningServerless and container designCloud securityFinOps and cost optimisation

Building an AI product and want to make sure the infrastructure decisions you make now do not become the bottleneck later?