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Makunike.com AI Twin vs Multichoice installer payments vs Picking & Packing app
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) → 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
Multichoice installer payments ›
R5M+ paid to installers in the first six months
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
At a glance
- Role
- Designer & Engineer
- When
- Oct 2026
- Length
- 1 mo
- Status
- Live
- Type
- AI, Web
- Role
- Integration engineer
- When
- Delivered
- Length
- —
- Status
- Delivered
- Type
- Platform
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
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.
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.
An Android app that drives picking and packing for warehouse and fulfilment operations.
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.
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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
- Payments integration
- Kotlin
- Android
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
- Processed over R5 million in installer payments within six months
- Led architecture and development
- Built in Kotlin for warehouse floor use
Infrastructure choices
- Vercel serverless (Next.js route handlers)
- Azure OpenAI
- Supabase Postgres + pgvector (HNSW)
- TypeSafe Jev
- Azure Blob Storage
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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.
Over R5 million in installer payments processed within six months.
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