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CV Coach vs CaseNotes vs Picking & Packing app
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: CV Coach (2025 – present) → Picking & Packing app (2025 – 2026) → CaseNotes (Feb 2026 – present)
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CV Coach ›
AI CV analysis, job matching and tailored applications
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
At a glance
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
The problem
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.
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
An Android app that drives picking and packing for warehouse and fulfilment operations.
What was built
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.
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.
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Stack
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- Kotlin
- Android
Highlights
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- 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
- Led architecture and development
- Built in Kotlin for warehouse floor use
Infrastructure choices
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- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
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Outcome
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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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