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Pairly vs ParcelNow 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: FastAPI, Next.js
- Both kinds of work: Web
- Order: ParcelNow (2025 – 2026) → CV Coach (2025 – present) → Pairly (Jul 2026 – present)
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Pairly ›
An AI-native operating system for founder-led teams
ParcelNow ›
On-demand parcel delivery with real-time tracking
CV Coach ›
AI CV analysis, job matching and tailored applications
At a glance
- Role
- Founder / Lead Engineer
- When
- Jul 2026 – present
- Length
- 4 mo so far
- Status
- In progress
- Type
- AI, Web, Platform
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
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.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
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
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.
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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
- Python
- FastAPI
- PostgreSQL
- Redis
- Next.js
- React
- Azure
- LangChain
- Docker
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
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
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
Infrastructure choices
- PostgreSQL
- Docker-based development and deployment standards
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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.
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