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Aamir Muhammad edited this page Mar 3, 2026 · 2 revisions

KodaAI: AI Efficiency Tracker

Elevate your engineering velocity

Comprehensive Requirements & Design Document

1. Project Overview

KodaAI is a performance analytics platform that quantifies the efficiency gains provided by AI-assisted development using Kiro. It bridges the gap between human estimation (Jira) and machine-accelerated output (GitHub/Kiro) to calculate a "Speed Multiplier" for software teams.

1.1 Intent Analysis

  • User Goal: Prove the ROI of AI tools to leadership and clients.
  • Core Problem: Traditional metrics (Velocity/Story Points) don't differentiate between human manual effort and AI-augmented efficiency.
  • Solution: A real-time tracker that uses hooks and API integrations to provide a granular view of time saved.

2. Technical Architecture

KodaAI uses a three-tier architecture to collect, process, and visualize efficiency data.

2.1 The Data Triangle

  • Jira Integration: Extracts the "Human Baseline" (Original Estimates).
  • GitHub Integration: Extracts the "Technical Reality" (LOC, PRs, Commits).
  • Kiro Hooks: Extracts the "AI Influence" (Prompt frequency, agent runtime).

3. Functional Requirements (FR)

FR1: Automated Tracking (Kiro Hooks)

  • FR1.1: Intercept every prompt sent to Kiro to track "AI Usage Density."
  • FR1.2: Log time spent by the Kiro Agent executing tasks.
  • FR1.3: Monitor file modifications in real-time to track LOC changes before they reach GitHub.

FR2: Jira & GitHub Sync

  • FR2.1: Automatically link code changes to Jira Tickets using branch name regex (e.g., feat/KODA-101).
  • FR2.2: Pull "Original Estimate" and "Story Points" to calculate the baseline.
  • FR2.3: Monitor GitHub PR cycle time from "First Commit" to "Merged."

FR3: The Speed Multiplier Dashboard

  • FR3.1: Display a real-time "Pulse" of current feature progress.
  • FR3.2: Visualize the "Efficiency Split" (Jira Estimate vs. Actual Time).
  • FR3.3: Generate a "Quality vs. Speed" scatter plot to ensure AI isn't compromising code health.

4. Technical Specifications & Schema

4.1 Core Speed Formula

The system calculates efficiency using the following logic:

$$SpeedMultiplier = \frac{T_{Jira}}{\sum (T_{Agent} + T_{HumanReview})}$$

4.2 API Data Contract (Kiro Hooks)

TypeScript

interface KiroTelemetry {
sessionId: string;
jiraTicket: string; // e.g. "KODA-502"
agentRuntimeMs: number; // Time AI was active
humanReviewMs: number; // Time dev spent in file after AI output
locChange: {
added: number;
deleted: number;
};
}

5. Visual Interface Design

The dashboard is designed for high-impact reporting.

visual interface

UI Components

  • The Pulse Row: Hero metrics showing the 3.8x (or similar) multiplier.
  • Efficiency Split: A stacked bar comparing the "Human Baseline" against the "AI-Augmented Reality."
  • Real-time Activity Feed: A scrolling log of Kiro Hook events (e.g., "Kiro Hook: +42 LOC added to auth.ts").

6. Architecture

architecture

7. Success Criteria

  • Metric Accuracy: Difference between reported LOC in KodaAI and GitHub must be < 1%.
  • Performance: Kiro Hooks must execute in under 50ms to avoid developer frustration.
  • Reporting Value: Customer-facing reports must be exportable in PDF, Excel, and HTML.

8. Next Steps & Implementation

  • Step 1: Establish the OAuth2 handshake for Jira and GitHub.
  • Step 2: Deploy the Kiro Hook listener to capture local IDE events.
  • Step 3: Build the aggregation engine to calculate the Speed Multiplier.

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