Kochura Deploy

Overview

Kochura Deploy explores a simpler way for small teams to deploy and operate bots and small applications. The current implementation is a product website and a working beta-application API.

This first stage connects product validation, frontend development, backend persistence and Linux infrastructure. Automated deployment, a self-service dashboard and billing remain future work.

Problem

Writing a bot or a microservice is only part of delivering it. It also needs a runtime, configuration, networking and a repeatable way to deploy changes.

For a small team, that operational work competes with product development. The current stage establishes an application and feedback workflow before investing in a deployment platform.

My role

I developed the product website and application workflow, implemented the backend and persistence layer, and worked on the containerized Linux deployment.

• Product scope, website and beta-application workflow
• Python / FastAPI backend and SQLite persistence
• Validation, notification handling and API tests
• Docker, Linux and reverse-proxy integration

Architecture

The web interface submits an application to a backend that validates and stores it. Notification delivery is separate from accepting the application. Docker and Linux support the hosting of this stack.

The diagram shows the current application responsibilities, not a deployment engine or live infrastructure topology.

Engineering decisions

The first design decision was to keep the implementation proportional to the current product question: collect useful applications before building a self-service deployment platform.

• Start with a lightweight website and application API to validate requirements before adding orchestration.
• Persist applications before sending notifications, so a delivery failure does not invalidate an accepted submission.
• Use SQLite with migration and backup checks to keep the initial storage footprint manageable.

Challenges

The work includes evolving application forms while preserving existing records, handling invalid submissions and keeping notification failures separate from successful persistence.

Another constraint is accurate product communication: the public website must distinguish the current onboarding service from future deployment capabilities.

Stack

HTML, CSS and JavaScript on the frontend; Python, FastAPI and SQLite in the application backend; Docker, Linux and reverse-proxy integration for hosting.

Result

A working product website and beta-application service, with persistence, notification handling and API tests. The public landing page is available below.

This stage demonstrates product scoping, frontend and backend development, data handling and infrastructure integration. It does not claim that automated deployment or billing has shipped.

Links