SCOPING GUIDE

AI workload architecture

Compare a managed API with a dedicated workload before taking on infrastructure operations.

Questions to resolve first

What data may leave your environment?
What workload and evaluation set can you measure?
Who owns access, monitoring, usage limits, and incident response?

One possible workflow

An internal document workflow may need a managed model API and a review queue rather than a dedicated GPU cluster.

Illustrative example, not a live integration or customer result. Feasibility depends on the systems, APIs, permissions, data, and support requirements reviewed during scoping.

What happens before a build?

We review the existing workflow and native platform features first. If custom development fits, scope, acceptance checks, milestones, pricing, and ongoing responsibilities are agreed before work starts.

Explore the engineering field notes →

Let’s move your project forward.

Tell us what you want to improve. We’ll review your goals and discuss the next step.

A USEFUL FIRST MESSAGE

“We want clearer answers about our services in search and AI discovery. Where should we improve our content and technical setup first?”

What happens after you send it?We review your description and reply with questions about your project. No calendar booking or newsletter signup.

Questions to resolve first

One possible workflow

What happens before a build?

Let’s move your project forward.

Your project, in a few sentences.