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Fashionly vs Makunike.com AI Twin 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
  • Both kinds of work: AI, Web
  • Order: Fashionly (Apr 2026 – present) → Pairly (Jul 2026 – present) → Makunike.com AI Twin (Oct 2026)

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Fashionly ›

Your style, elevated: an AI stylist that starts with what you own

Makunike.com AI Twin ›

This website: a portfolio you can interview by phone call or chat

Pairly ›

An AI-native operating system for founder-led teams

At a glance

Role
Founder / Lead Engineer
When
Apr 2026 – present
Length
7 mo so far
Status
MVP
Type
AI, Mobile, Web
Role
Designer & Engineer
When
Oct 2026
Length
1 mo
Status
Live
Type
AI, Web
Role
Founder / Lead Engineer
When
Jul 2026 – present
Length
4 mo so far
Status
In progress
Type
AI, Web, Platform

The problem

Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
Portfolios are static, but hiring conversations aren't. A recruiter wants to know whether I've shipped mobile apps, a hiring manager wants a production story in STAR form, and a sourcing agent wants structured answers it can compare. A PDF CV can't do any of that.
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

Fashionly grounds recommendations in the user's wardrobe, builds outfits from existing items and recommends products only where a genuine wardrobe gap exists. Khanya, a LangGraph stylist with about 15 typed tools, handles wardrobe access, catalogue search, saved looks and human-stylist bookings.
A digital version of me that holds a real interview by phone-style voice call or text, grounded only in material I curated. It changes what's on screen as it answers, logs what it can't answer so I can teach it, records and QA-scores every call, and is reachable by other AI agents over MCP and A2A.
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
  • Kotlin
  • Supabase
  • LangGraph
  • MongoDB
  • SSE
  • Next.js 15
  • React 19
  • Tailwind CSS v4
  • GPT-6.1 (Azure, Responses API)
  • gpt-realtime-2.1
  • WebRTC
  • text-embedding-3-small
  • Supabase pgvector
  • TypeSafe Jev
  • MCP
  • A2A
  • Azure Blob
  • LangSmith
  • Vercel
  • Python
  • FastAPI
  • PostgreSQL
  • Redis
  • Next.js
  • React
  • Azure
  • LangChain
  • Docker

Highlights

  • Khanya: a LangGraph tool-calling stylist with about 15 typed tools
  • MongoDB-checkpointed conversation memory and SSE streaming of tool progress and grounded product cards
  • Request-scoped Supabase clients with row-level security on every request
  • Generative UI: the model calls typed navigate and show_panel tools, streamed to the browser as NDJSON events
  • Phone-call voice on gpt-realtime-2.1 over WebRTC, with every call recorded, transcribed and QA-scored
  • TypeSafe Jev in ten places: routing, tool selection, interview mode, guards, lead scoring, grounding, triage, FAQ review, agent vetting and call QA
  • Self-improving RAG: weak or ungrounded answers land in a ranked CMS inbox and become FAQs
  • MCP and A2A servers with request vetting and rate limits for agent-to-agent interviews
  • 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

  • Supabase with RLS
  • MongoDB checkpoints
  • SSE
  • Vercel serverless (Next.js route handlers)
  • Azure OpenAI
  • Supabase Postgres + pgvector (HNSW)
  • TypeSafe Jev
  • Azure Blob Storage
  • PostgreSQL

Outcome

A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
The twin never invents a fact about my career: when it doesn't know, it says so and I'm notified. Intent-matched navigation fires within about a second, before the first answer token. Every call is recorded, transcribed and scored so review starts with the calls that need it, and one knowledge base serves text, voice, MCP and A2A.
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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