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AI engineers

Not one chatbot. A team, each with a job.

Behind every request is a team of specialist AI engineers. They divide the work the way a real platform team would, and nothing is built until it has been checked.

How a request is handled

From one sentence to a checked deployment.

The team lead reads your request, writes a plan and decides which specialists are needed. They work in parallel. The DevOps engineer turns their advice into a concrete deployment, and the QA engineer checks it against the rules of the platform it will run on.

Only then does microbuilder create anything, in your account, with every step shown live.

  • Plans for ECS, EKS or K3s, with the platform’s real limits built in
  • Databases, caches and brokers sized and kept private
  • Public behind a load balancer, or private behind a Cloudflare tunnel

The team

Who does what.

Team lead

Every request

Reads the request, writes the plan and decides who works on it.

AWS engineer

When no platform is chosen

Chooses the platform and the AWS services when you have not picked one.

Cloudflare engineer

When you ask for edge features

DNS, tunnels, caching, rate limits and Zero Trust access at the edge.

Observability engineer

When you ask for monitoring

Metrics, logs, health checks and alerts for what is being built.

Data engineer

When the plan needs data

Databases, caches and message brokers: engine, sizing, backups, access.

DevOps engineer

Every deployment

Turns the plan into a real deployment: containers, ports, volumes, secrets, scaling.

QA engineer

Every deployment

Checks the deployment before anything is created, and sends back what fails to be fixed.

Designer

Microsites

Designs and writes complete websites for microsites.

How they work

Built to be trusted with production.

Only who the job needs

A plain “deploy nginx” goes from the team lead to DevOps to QA. Specialists join only when the request mentions a database, the edge or monitoring, so you are not paying for calls that add nothing.

Written playbooks

Each engineer works from a playbook of rules learned the hard way: health checks that pass, Fargate sizes that exist, secrets that never land in plain text. Only the rules relevant to your request are loaded.

Checked before it is built

The QA engineer validates every deployment before a resource is created and sends back what fails. Problems are caught on paper, not in your AWS bill.

Nothing hidden

You see the plan, the live build log and every resource in your own console. Every AI call can be traced in your own Langfuse.

Your key, your model

The engineers run on the AI provider and model you choose: Anthropic, OpenRouter, Cloudflare AI Gateway or your own vLLM endpoint. Billed by your provider, with no markup.

Change it by asking

Add a service, change a setting or fix a failing app by describing it. Edits go through the same build-and-check loop as the first deploy.

Give the team its first job.

Connect a cloud account, describe what you need, and watch it build. The free plan is free for good.

Start freeSee pricing

Free plan. No credit card.