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Multichoice installer payments vs CaseNotes vs CV Coach
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) → CaseNotes (Feb 2026 – present)
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Multichoice installer payments ›
R5M+ paid to installers in the first six months
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
CV Coach ›
AI CV analysis, job matching and tailored applications
At a glance
- 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
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
The problem
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.
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.
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.
Stack
- Payments integration
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
Highlights
- 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
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
Infrastructure choices
—
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
—
Outcome
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.
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