Enterprise Developer Portal + AI Chatbot

Overview

I led end-to-end UX research and product engineering for an enterprise developer portal — identifying the tooling gaps through user interviews, defining the product, then architecting and personally delivering 87% of the implementation (173 of 199 commits) across a React 19/TypeScript front end and a Flask REST API.

The engagement demanded true end-to-end ownership: research, product definition, design system, front end, API, CI/CD, and cloud infrastructure — shipped as a scoped MVP with 5+ core features in under 4 months.

Problem

Developers across the organization lacked a unified place to discover tooling, request access, track platform status, and get help. Knowledge lived in tribal memory and scattered docs; support was ad hoc; and every new team repeated the same discovery pain.

• No single entry point for platform services, documentation, or support.
• Repetitive questions consumed senior engineers' time.
• New capabilities shipped without a consistent UI, eroding trust and usability.
• Strict security and Section 508 accessibility requirements ruled out off-the-shelf answers.

Approach

research_first

• User interviews and iterative build/test/deploy cycles surfaced the tooling gaps that directly shaped the portal concept.
• Prototyped early and validated with real users rather than committing to a speculative feature list.

spec_driven_ai_development

• Created a spec-driven, AI-assisted development methodology in one week: 42 prescriptive engineering specs (40,000+ lines) spanning UX, backend, security, quality, and infrastructure.
• The specs act as guardrails and shared context, letting AI coding agents scaffold features consistently — the same patterns, tokens, and quality bar on every feature.

accessibility_by_design

• Section 508 / WCAG 2.1 enforced across every screen and component from the start — including the AI chatbot experience — not retrofitted at the end.

Solution

• Developer portal MVP: 15-page application with 5+ core features, shipped in under 4 months.
• In-app AI chatbot: custom React chat UX with a Flask routing/config layer, integrating conversational AI directly into the core product experience.
• Customizable dashboard: drag-and-drop widget layout (react-grid-layout) with a runtime widget registry and Redux Toolkit state management.
• REST API: Flask application-factory architecture — 9 data models, 15 endpoints, 10 services, Marshmallow validation, centralized error handling.
• Delivery pipeline: multi-stage GitLab CI/CD (lint → build → containerize → deploy) with Kaniko rootless builds, manual promotion gates, semantic-version tagging, and automated health checks.
• Infrastructure: Azure provisioning and a production-grade Helm chart (HPA, Istio, FluxCD GitOps, Key Vault CSI) enabling a phased Kubernetes migration.

The "Command Center" Design System

I designed a full dark-theme design system — tokens, color palettes, an 8-pt spacing scale, elevation shadows, and a type scale — with MUI component overrides and a persistent runtime theme switcher. (This portfolio uses a miniature version of the same approach: view source.)

• 70-component React 19/MUI v9 library used across the 15-page application.
• 1,350-line design-system spec enforcing card patterns, motion, and responsive standards for cross-team consistency.
• Storybook documentation for interactive, self-serve component browsing.
• Published as an npm package so other teams can adopt it in their own products without handholding.

Impact

• Concept → scoped MVP shipped in under 4 months.
• 87% of commits (173/199) personally delivered across the full stack.
• Design system adopted by other development teams via npm + Storybook.
• Conversational AI integrated into the product day one — 508-compliant.
• The spec-driven methodology became a reusable delivery asset beyond this project.

Reflection

• What worked: writing prescriptive specs before code multiplied output without sacrificing consistency — AI agents are only as good as the guardrails and context you design for them.
• Tradeoffs: a hard MVP deadline meant deliberately deferring integrations and advanced analytics to post-MVP phases.
• What I learned: the highest-leverage design artifact on an AI-assisted team isn't a mockup — it's the system of tokens, patterns, and specs that makes every contributor (human or agent) produce the same quality of UI.