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CaseNotes vs CV Coach vs ParcelNow
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
- Both kinds of work: Web
- Order: CV Coach (2025 – present) → ParcelNow (2025 – 2026) → CaseNotes (Feb 2026 – present)
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CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
CV Coach ›
AI CV analysis, job matching and tailored applications
ParcelNow ›
On-demand parcel delivery with real-time tracking
At a glance
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
The problem
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
A Chrome extension captures job ads; a LangGraph workflow parses the CV and the ad, scores the match and drafts tailored cover letters and CV revisions.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
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.
A Chrome extension captures advertisements, stores them in PostgreSQL and queues analysis. The platform compares a CV with a job description, produces a structured match score and generates tailored cover letters and CV revisions.
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Stack
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
Highlights
- 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
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
Infrastructure choices
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
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- Docker-based development and deployment standards
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.
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