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CaseNotes vs WDDNG vs Pairly
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: Next.js, PostgreSQL
- Both kinds of work: AI, Web
- Order: WDDNG (Jan 2026 – present) → CaseNotes (Feb 2026 – present) → Pairly (Jul 2026 – present)
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CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
WDDNG ›
South Africa's wedding marketplace, with two apps on Google Play
Pairly ›
An AI-native operating system for founder-led teams
At a glance
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- Role
- Founder / Full-stack & Mobile Engineer
- When
- Jan 2026 – present
- Length
- 10 mo so far
- Status
- Live
- Type
- AI, Mobile, Web
- Role
- Founder / Lead Engineer
- When
- Jul 2026 – present
- Length
- 4 mo so far
- Status
- In progress
- Type
- AI, 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.
South African couples plan across fragmented directories, WhatsApp messages and opaque “price on request” listings, while many international tools don't support local multi-ceremony wedding journeys.
Small teams stitch together disconnected tools for websites, customer communication, content, analytics and operations, while generic AI assistants lack safe access to business context and actions.
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 Next.js and Supabase marketplace with two Kotlin / Jetpack Compose Android apps on Google Play: one for couples, one for vendors. Thuli, the AI planner, is a LangGraph supervisor coordinating 16 specialist agents.
Pairly gives each business one governed platform where specialised agents can work across those functions. It combines tenant-scoped AI agents, websites and CMS, CRM and communications, newsletters, analytics, integrations and a skills marketplace.
Stack
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- Next.js
- Supabase
- PostgreSQL
- Kotlin
- Jetpack Compose
- LangGraph
- Zod
- Paystack
- Azure
- LangSmith
- Python
- FastAPI
- PostgreSQL
- Redis
- Next.js
- React
- Azure
- LangChain
- 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
- Two Kotlin / Jetpack Compose Android apps published on Google Play
- Thuli: a LangGraph supervisor coordinating 16 specialist agents through Zod-typed handoff tools
- Per-agent model selection, token metering and LangSmith tracing
- SSE streaming that renders vendor cards and progress during an answer
- Tenant-scoped agent runtime with durable memory, model routing, typed tools and permission ceilings
- Streaming agent UX with human approval gates, audit events and token budgets
- Installable skills marketplace with automated validation and human publication review
- End-to-end features spanning API, CMS, renderer, email and PDF output
Infrastructure choices
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
- Supabase with PostgreSQL RLS
- PostgreSQL
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.
The marketplace runs across web and two production Android apps, giving couples structured planning tools and vendors better-qualified enquiries with date, budget and guest-count context.
A broad working platform with multi-tenant permissions, real-time agent execution, publishing and revision workflows, provider integrations and installable skills, with approval gates, audit events and human review for consequential AI actions.