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CaseNotes vs WhatsApp platforms 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
- Both kinds of work: AI
- Order: WhatsApp platforms (2018 – 2022) → CV Coach (2025 – present) → CaseNotes (Feb 2026 – present)
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CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
CV Coach ›
AI CV analysis, job matching and tailored applications
At a glance
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- Role
- Lead engineer, Mmogo Media
- When
- 2018 – 2022
- Length
- 4 yr
- Status
- Delivered
- Type
- Platform, AI
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
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 WhatsApp-based job platform (Altur), Harambee Youth Accelerator's move from a mobisite to WhatsApp, and eButler's WhatsApp ordering with payments through Flash.
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.
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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
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- Python
- Django
- WhatsApp Business API
- Infobip
- Twilio
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
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
- Conversational journeys on the WhatsApp Business API
- Payments via Flash, Paystack and MTN MoMo
- 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
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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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