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Makunike.com AI Twin vs Fashionly vs ParcelNow

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: Web
  • Order: ParcelNow (2025 – 2026) → Fashionly (Apr 2026 – present) → Makunike.com AI Twin (Oct 2026)

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Makunike.com AI Twin ›

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

Fashionly ›

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

ParcelNow ›

On-demand parcel delivery with real-time tracking

At a glance

Role
Designer & Engineer
When
Oct 2026
Length
1 mo
Status
Live
Type
AI, Web
Role
Founder / Lead Engineer
When
Apr 2026 – present
Length
7 mo so far
Status
MVP
Type
AI, Mobile, Web
Role
Technical lead, Smart Kitchen Co
When
2025 – 2026
Length
1 yr
Status
Delivered
Type
Web, Platform

The problem

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.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.

What was built

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.
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.
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Stack

  • 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
  • Next.js
  • Kotlin
  • Supabase
  • LangGraph
  • MongoDB
  • SSE
  • Next.js
  • FastAPI
  • PostgreSQL
  • Redis
  • Docker

Highlights

  • 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
  • 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
  • Next.js dashboards over FastAPI services
  • Live delivery updates through logistics and messaging integrations

Infrastructure choices

  • Vercel serverless (Next.js route handlers)
  • Azure OpenAI
  • Supabase Postgres + pgvector (HNSW)
  • TypeSafe Jev
  • Azure Blob Storage
  • Supabase with RLS
  • MongoDB checkpoints
  • SSE
  • Docker-based development and deployment standards

Outcome

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 working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
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