AI-assisted KPI management system: 4 months of development and still no working application. Advice needed.

[peteshop01-boop]

Hi everyone,
I'm looking for practical advice from experienced developers who have built business applications using ChatGPT, Codex or similar AI coding tools.
I'm an operations manager at a UK charity, with a background in technology project management, but I'm not a software developer.
For the past 3–4 months, I've been developing a KPI management system using AI. We've generated significant amounts of code, automated tests, documentation and various builds, but still haven't delivered a reliable, working end-to-end application.
I'm beginning to question whether the development process, choice of platforms or overall approach is fundamentally wrong.
What I'm trying to build
A KPI management system for approximately 25 employees that:
Allows staff to create individual workplans and KPIs.
Supports manager review, approval and amendments.
Records quarterly progress, targets, outcomes and supporting evidence.
Links individual KPIs to organisational strategic objectives.
Automatically calculates performance and RAG ratings.
Produces interactive management dashboards and Board reports.
Includes appropriate permissions, audit trails and version control.
Works reliably on desktop and mobile.
Platforms and technology currently being used
ChatGPT
OpenAI Codex
GitHub
Microsoft 365
Microsoft Power Apps / Power Platform
Microsoft SharePoint
Cloudflare Workers
JavaScript / HTML / CSS
Node.js
Windows Mini PC
Desktop Commander (MCP)
Restrictions and requirements
Preferably use existing Microsoft 365 infrastructure and licensing.
Keep hosting and ongoing costs low.
Ensure appropriate security for employee information.
Maintain separate development, testing and production arrangements.
Deliver something that non-technical staff can maintain and operate.
Avoid the need for an ongoing full-time developer.
Where things have gone wrong
We've spent months developing different components of the system.
Automated tests have passed, but the complete application has not reached an acceptable working state.
There are numerous local files, scripts, different builds and development records.
We have a GitHub repository, but I'm not confident that our development process consistently maintains one authoritative, up-to-date codebase.
AI has effectively acted as developer, tester and project manager, but progress has become fragmented. Development work doesn't consistently translate into tested, usable functionality.
Despite extensive development activity, we still don't have a complete application that staff can use.
Questions I'd appreciate advice on
Are these the right platforms for this type of application, or have I overcomplicated the architecture?
Would you build this in Power Apps, as a custom web application, or using another platform entirely?
What is the most effective way to organise an AI-assisted software development project using Codex?
How should I structure GitHub, local development, testing and deployment so everything stays synchronised?
How do experienced developers prevent AI coding agents from producing disconnected code and repeatedly rebuilding or breaking previously completed functionality?
Would you recommend auditing and recovering the existing code, or starting again with a simpler approach?
What would your first five steps be if you inherited this project today?
Realistically, how long should a competent developer take to deliver something like this?
I'm open to changing platforms, simplifying the requirements or adopting a completely different development process.
What I'm really looking for is a proven approach to taking an AI-assisted project from requirements through development, testing and deployment, without losing control of the code or continuously revisiting completed work.

[TheodorNEngoy]

I’m building Turnfeed with Codex, and passing tests didn’t catch several problems I found by using the deployed site. I’d start by making one complete workflow work reliably before adding more.

My first five steps would be:

1. Preserve the existing files, choose one repository and branch as the source of truth, and record which commit is actually deployed.
2. Write one acceptance path with dummy data: a staff member creates a KPI → submits it → a manager approves → progress is updated → the dashboard reflects the saved result.
3. Run that path in a separate test environment using both roles, refreshing or reopening after each change. Check that each role cannot perform the other’s restricted actions.
4. Give Codex the first failing step, expected versus actual result, and relevant error. Ask it to trace the existing implementation and fix that one failure.
5. Rerun the whole path before expanding scope.

I haven’t seen your code, so I wouldn’t choose a new platform or estimate delivery time yet. What is the first concrete failure in that smallest workflow?

[bomtobe-hgkim]

Given the approval and audit requirements, I’d make the first vertical slice one employee and one KPI: draft → manager amendment/approval → quarterly update with evidence → RAG calculation → one dashboard. Before generating more features, hand-write a tiny fixture with expected ratings, including a boundary value, missing evidence, and a changed target. Keep the approved target and evidence version visible in the result so a dashboard number can be traced to its source. That makes the “one end-to-end workflow” advice testable. I work on Orke at BOMTOBE, a desktop workspace for Claude Code and Codex; I haven’t seen or run your app.

[landjunge]

Based on my own experience with AI-assisted development, your description gives me the impression that the project may have grown incrementally: new features and tools were added while the architecture evolved alongside them. That can easily happen, especially with a substantial business application like yours. Permissions, approvals, version history, calculations and reporting all need to work individually and reliably together.

I’ve had to restart some of my own projects because further patching of the existing code was no longer producing meaningful progress. It may be worth considering that possibility too—but your description alone isn’t enough to determine whether it would be appropriate here.

For a more specific assessment, I would need to examine the code, its size and, especially, its structure and dependencies. A large, tightly coupled codebase can make targeted fixes considerably harder. However, line count alone doesn’t determine whether an AI coding tool can work effectively on it.

Were the requirements, core architecture and scope of the first usable version established at the beginning, or did they grow during development? How large is the codebase, and how is it divided into modules?

A sanitised project overview and one concrete failing workflow would be a useful starting point for comparing recovery of the existing implementation with a clearly scoped rebuild.