AI engineering & automation

Less repetitive work.
More engineering
that matters.

Put AI agents to work on software maintenance, vulnerability remediation and engineering workflows. We build the integrations, execution environments and validation steps that turn individual tools into dependable automation.

Built around your repositories, infrastructure and review process.

AGENTS, WITH A WORKFLOWFIG. 01
From backlog to review.
Automation that fits how your team ships.
Identify workScan findings, dependencies, repeatable tasks
SCOPE
Run agentsIsolated jobs with project-specific instructions
EXECUTE
Validate changesBuild, test and report the outcome
CHECK
Prepare for reviewPull requests. Clear summaries. Human decisions.
SCOPED TASKS · ISOLATED EXECUTIONCOST VISIBILITY
Built on familiar foundations
Coding agentsGitHub & CI/CDKubernetesAWSPython & TypeScript

01 / Expertise

AI automation.
Engineering underneath.

Automate repeatable engineering work, backed by the software, cloud and reliability expertise needed to run it.

02 / The approach

From repeatable task
to working automation.

Start with a bounded task and a clear definition of success. Build the workflow around your team’s tools, checks and approval process.

How infra-dev works
  1. 01

    Find the repeatable task.

    Identify the backlog, required access and constraints. Agree on which tasks an agent can handle and when a person needs to decide.

  2. 02

    Build the workflow.

    Connect the tools, configure isolated runners and define validation steps. Make failures, costs and proposed changes visible.

  3. 03

    Validate the result.

    Evaluate the output against real tasks. Review the changes, measure useful outcomes and hand over a workflow your team can operate.

03 / A practical fit

Your team has a backlog.
Not everything needs to be manual.

Maintenance keeps slipping down the backlog. Turn dependency updates and vulnerability findings into scoped tasks and reviewable pull requests.

Your engineers repeat the same steps across repositories. Build reusable workflows around project-specific instructions, tooling and checks.

Your AI experiment needs to work in production. Add scheduling, isolated execution, concurrency controls and cost visibility.

A good place to start

What would you take
off your team’s backlog?

Tell us what you’re working with and what you’d like to improve.

Let’s work through it