AI Development Pipeline Implementation
We bring a ready-made production system to your project: Jira, Git workflow, Docker, staging and production environments, AI agents and double AI review. The result — a single command, make CHAT-XXX, opens a task and starts production in parallel.
Implement the AI pipelineHow it works
Jira as the control panel
We install and configure Jira and hook up CLI automation: tickets are created from the terminal, statuses move automatically on commit and deploy. Every task is visible from idea to production.
Git workflow and worktrees
Every task gets its own branch and an isolated git worktree with its own environment. Commits follow Conventional Commits with the ticket number. Parallel tasks never conflict.
Docker architecture
Every service is a separate container: API, frontend, database, workers, Nginx. The very same image goes to the staging and the production server. Docker Compose for dev and prod.
Staging and production
An isolated staging environment with its own database and domain — where your QA tester accepts tasks. Staged deploy: branch checks → staging → automated tests → production with a health-check and rollback.
Design patterns
We define the architectural patterns for your product and fix them in the project rules. AI agents follow the patterns, and the architecture reviewer catches violations — the codebase doesn't drift.
Self-healing environment
A dev-doctor script brings up and repairs the environment by itself: containers, dependencies, migrations, builds. The stand revives automatically whenever a task is opened — no more "it doesn't start on my machine".
Implementation timeline — 2–4 weeks
Week 1: audit and Jira
We analyze the project and the stack, deploy Jira with the workflow and CLI automation, and agree on the task format.
Week 2: Docker and environments
We containerize the services, bring up staging and production, and configure staged deploy with rollback.
Week 3: pipeline and AI
We wire up the make commands, AI agents, double AI review and automated tests. Design patterns get fixed in the project rules.
Week 4: live run
Real tasks go through the full cycle under our supervision. We hand over the documentation and train your team.
The pipeline is already shipping a real product
We use this exact system to build CHATBOSS.PRO every day: tasks are opened with make CHAT-XXX, run in parallel waves and reach production through the staging environment. Not a concept — our actual production line.
Frequently Asked Questions
Will the pipeline fit our stack?
The pipeline is stack-agnostic: Jira, Git, Docker and AI agents work with any language or framework. Ours runs a TypeScript monorepo of 5 repositories, but the scheme transfers to PHP, Python, Go and other stacks.
We already have repositories and processes. Do we break everything?
No. We integrate into your existing repositories: the Makefile, the worktree workflow, the environments and AI reviews are added on top of what you have. Your history and current branches stay untouched.
What does it cost to run?
Infrastructure starts at one VPS (from $10/month) plus AI model tokens billed per actual use. No recurring fees to us: the system is fully yours.
What exactly do we get?
A working system: make CHAT-XXX opens a task with its environment, AI agents write the code, two AI reviewers check it, staged deploy ships it to staging and production. Plus documentation and a trained team.
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