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WDDNG vs Multichoice installer payments 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
- Order: WDDNG (Jan 2026 – present) → CaseNotes (Feb 2026 – present)
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WDDNG ›
South Africa's wedding marketplace, with two apps on Google Play
Multichoice installer payments ›
R5M+ paid to installers in the first six months
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
- Integration engineer
- When
- Delivered
- Length
- —
- Status
- Delivered
- Type
- Platform
- 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.
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.
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
- Payments integration
- 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
- Processed over R5 million in installer payments within six months
- 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.
Over R5 million in installer payments processed within six months.
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.