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Pairly vs CV Coach vs CaseNotes
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: FastAPI, Next.js, LangChain
- Both kinds of work: AI, Web
- Order: CV Coach (2025 – present) → CaseNotes (Feb 2026 – present) → Pairly (Jul 2026 – present)
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Pairly ›
An AI-native operating system for founder-led teams
CV Coach ›
AI CV analysis, job matching and tailored applications
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
At a glance
- Role
- Founder / Lead Engineer
- When
- Jul 2026 – present
- Length
- 4 mo so far
- Status
- In progress
- Type
- AI, Web, Platform
- 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
The problem
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.
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.
What was built
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.
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.
Stack
- Python
- FastAPI
- PostgreSQL
- Redis
- Next.js
- React
- Azure
- LangChain
- Docker
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
Highlights
- 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
- 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
Infrastructure choices
- PostgreSQL
—
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
Outcome
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.
—
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.