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OrderEase vs CaseNotes vs Joel Transport
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: Web
- Order: Joel Transport (Dec 2022 – May 2023) → OrderEase (2025 – 2026) → CaseNotes (Feb 2026 – present)
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OrderEase ›
Branded online ordering for restaurants and retailers
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
Joel Transport ›
Online booking platform on AWS
At a glance
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- Role
- Lead Engineer
- When
- Dec 2022 – May 2023
- Length
- 6 mo
- Status
- Live
- Type
- Web, Platform
The problem
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
Centralised the customer journey and operational data, reduced manual hand-offs and set up a documented AWS foundation.
What was built
—
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.
Led architecture and hands-on engineering for an online booking platform: Python APIs and the AWS deployment architecture, integrated with Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ.
Stack
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- Python
- AWS
- Paystack
- Sage
- Zoho
Highlights
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- 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
- Python APIs and AWS deployment architecture
- Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ integrations
Infrastructure choices
—
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
- AWS
Outcome
—
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.
Centralised the customer journey and key operational data, reduced manual hand-offs and established a documented AWS foundation for future development.