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OrderEase vs ParcelNow vs CaseNotes
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: Next.js, FastAPI, PostgreSQL, Redis
- Both kinds of work: Web
- Order: OrderEase (2025 – 2026) → ParcelNow (2025 – 2026) → CaseNotes (Feb 2026 – present)
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OrderEase ›
Branded online ordering for restaurants and retailers
ParcelNow ›
On-demand parcel delivery with real-time tracking
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
At a glance
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
The problem
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
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
—
—
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
- FastAPI
- PostgreSQL
- Redis
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
Highlights
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
- 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
—
- Docker-based development and deployment standards
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
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.