Systems & Infrastructure
Done-for-youAIdevinfrastructure
fortechnicalteams.
Turn your developers into 10x AI engineers using the same systems, configs, and infrastructure we use to run our consulting practice. We built it for ourselves first. Now we deploy it for you.
The Stack
Three layers.
All production.
Most teams get stuck at the same point: they've seen what AI can do in a demo, but they don't have the infrastructure to make it run reliably in their actual workflow.
We built that infrastructure for ourselves first. Every client engagement runs on the same three layers.
Layer 01
AI Coding Configs
Your team’s AI is only as good as its configuration.
Out of the box, tools like Claude Code, Cursor, Amp, Codex, and OpenCode are powerful but generalized. They don’t know your codebase, your conventions, or the things your team cares about. When they produce bad output, the issue is usually configuration, not capability.
We build custom AI coding configurations for development teams. These are systems that enforce your standards in real time, get more intelligent as you use them, and are customizable at both the admin level and the individual developer level. They generalize across any set of supported coding agents.
- Hooks that enforce your standards before code gets committed
- Skills that encode your team’s actual patterns and workflows
- LSP integration so the model understands your types, your APIs, your architecture
- CLI tooling that wraps common operations into repeatable commands
- Forbidden patterns so the model never produces code your team has agreed to avoid
Every developer on your team works with the same AI configuration. You improve it together. The model gets better as your team uses it, not worse.
Layer 02
Agent Deployment Platform
An AI agent is only useful if you can run it from the places where work actually happens.
You need to trigger agents from Slack messages. From Linear issues. From GitHub comments. From emails. From cron jobs at 2 AM. Each one needs the right system prompt, the right skills, the right authentication, and the right guardrails.
We built a standardized deployment layer that handles all of this:
- One config per agent — system prompt, skills, auth, and tool access defined in one place
- Multi-source triggers — Slack, Linear, GitHub, email, webhooks, scheduled runs
- Isolated execution — each agent runs with exactly the permissions it needs and nothing else
- Version control — agent configs are code, reviewed and deployed like any other infrastructure
This is the base layer for most of what we build. It’s why our systems run reliably and why they’re maintainable after we leave.
Layer 03
Always-On Cloud Agents
Agents that run without anyone asking them to.
We set up VPS instances that run long-lived agent processes. Orchestration harnesses manage parallel execution. Pipelines chain multi-step workflows. And because these run on your infrastructure, not ours, you own them.
What this looks like in practice:
- Bug triage — a Linear issue gets created, an agent reproduces it, opens a PR with a fix, and tags the right reviewer
- Code review — push to a branch, agents run security scans, pattern checks, and architectural review before a human looks at it
- Knowledge work — agents process incoming emails, update CRMs, generate reports, and flag anything that needs a human decision
Every one of these can be fixed, extended, or shut down from a Slack message or a Linear comment. You’re never waiting on us.
Start here.
We maintain a few open repos and config sets that show how these systems work. They're not marketing material. They're the actual starting points we use.
Repo
AI Coding Starter Config
Hooks, skills, and config templates for teams getting started with AI-assisted development. Includes our editorial accent system, Vale prose linting, and commit hooks.
View on GitHubRepo
Agent Deployment Examples
Three working examples: a Slack-triggered agent, a GitHub issue responder, and a scheduled report generator. All deployable to any VPS with Docker.
View on GitHubRepo
Pipeline Templates
Multi-stage pipeline definitions for common workflows: PR review, bug triage, and content generation. Built on our orchestration system.
View on GitHubThese aren't demos. They're production configs with the API keys removed.
For teams that already write code.
If you have developers, you don't need another AI platform. You need someone who's already built the infrastructure and can set it up for your team in weeks, not quarters.
01
Custom AI Coding Config
We audit how your team works. We read your codebase. We build a config that includes hooks, skills, forbidden patterns, and CLI tools specific to your stack and conventions. Every developer gets the same setup. You iterate on it together.
Most teams see the difference in the first week.
02
Agent Deployment Setup
We build out your agent infrastructure: the deployment layer, the trigger integrations (Slack, Linear, GitHub, email, whatever you use), and the first set of production agents. We train your team to add new ones.
You own everything. No vendor lock-in. No monthly platform fee.
03
VPS + Orchestration Buildout
We set up dedicated cloud instances for long-running agent work. Parallel execution harnesses. Pipeline orchestration. Monitoring. The infrastructure that lets you run agents at scale without managing a distributed system yourself.
This is what turns AI from “that thing we tried” into a permanent part of how your team operates.
Questions
Common questions from technical teams.
Your team writes the code. We'll build the systems around it.
Tell us about your stack, your team, and where you want AI to actually do something useful. We'll tell you what's realistic and what it would take.
No sales process. First call is with the founder.