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Picking & Packing app vs Pairly 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: Picking & Packing app (2025 – 2026) → CV Coach (2025 – present) → Pairly (Jul 2026 – present)
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Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
Pairly ›
An AI-native operating system for founder-led teams
CV Coach ›
AI CV analysis, job matching and tailored applications
At a glance
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
- 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
The problem
An Android app that drives picking and packing for warehouse and fulfilment operations.
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.
What was built
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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.
Stack
- Kotlin
- Android
- Python
- FastAPI
- PostgreSQL
- Redis
- Next.js
- React
- Azure
- LangChain
- Docker
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
Highlights
- Led architecture and development
- Built in Kotlin for warehouse floor use
- 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
Infrastructure choices
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- PostgreSQL
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Outcome
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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.
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