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WDDNG vs Picking & Packing app vs CaseNotes
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: none in common
- Both kinds of work: Mobile
- Order: Picking & Packing app (2025 – 2026) → WDDNG (Jan 2026 – present) → CaseNotes (Feb 2026 – present)
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WDDNG ›
South Africa's wedding marketplace, with two apps on Google Play
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
At a glance
- Role
- Founder / Full-stack & Mobile Engineer
- When
- Jan 2026 – present
- Length
- 10 mo so far
- Status
- Live
- Type
- AI, Mobile, Web
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
The problem
South African couples plan across fragmented directories, WhatsApp messages and opaque “price on request” listings, while many international tools don't support local multi-ceremony wedding journeys.
An Android app that drives picking and packing for warehouse and fulfilment operations.
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
What was built
A Next.js and Supabase marketplace with two Kotlin / Jetpack Compose Android apps on Google Play: one for couples, one for vendors. Thuli, the AI planner, is a LangGraph supervisor coordinating 16 specialist agents.
—
An integrated research, e-discovery and learning platform: hybrid semantic search across more than 10,000 judgments, a precedent citation graph, structured document extraction, AI-assisted discovery and stateful research workflows.
Stack
- Next.js
- Supabase
- PostgreSQL
- Kotlin
- Jetpack Compose
- LangGraph
- Zod
- Paystack
- Azure
- LangSmith
- Kotlin
- Android
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
Highlights
- Two Kotlin / Jetpack Compose Android apps published on Google Play
- Thuli: a LangGraph supervisor coordinating 16 specialist agents through Zod-typed handoff tools
- Per-agent model selection, token metering and LangSmith tracing
- SSE streaming that renders vendor cards and progress during an answer
- Led architecture and development
- Built in Kotlin for warehouse floor use
- Five-stage Graph RAG pipeline in LangGraph
- Hybrid pgvector and Neo4j retrieval, and a TAR active-learning loop
- Human-in-the-loop tool-calling chat with PostgreSQL checkpoints
- MCP server for case search and drafting
Infrastructure choices
- Supabase with PostgreSQL RLS
—
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
Outcome
The marketplace runs across web and two production Android apps, giving couples structured planning tools and vendors better-qualified enquiries with date, budget and guest-count context.
—
An integrated research and review platform grounded in more than 10,000 judgments, with a structured learning bank of 1,624 rubric-scored questions. The architecture supports resumable jobs, traceable sources and human review.