Compare
OrderEase vs Pairly
The problem, what was built, the stack and the outcome, next to each other.
In common
- Shared stack: Next.js, FastAPI, PostgreSQL, Redis
- Both kinds of work: Web, Platform
- Order: OrderEase (2025 – 2026) → Pairly (Jul 2026 – present)
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OrderEase ›
Branded online ordering for restaurants and retailers
Pairly ›
An AI-native operating system for founder-led teams
At a glance
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
- Role
- Founder / Lead Engineer
- When
- Jul 2026 – present
- Length
- 4 mo so far
- Status
- In progress
- Type
- AI, Web, Platform
The problem
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
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
—
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
- Redis
- Python
- FastAPI
- PostgreSQL
- Redis
- Next.js
- React
- Azure
- LangChain
- Docker
Highlights
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- 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
—
- PostgreSQL
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.