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WhatsApp platforms vs Pairly

The problem, what was built, the stack and the outcome, next to each other.

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In common

  • Shared stack: Python
  • Both kinds of work: Platform, AI
  • Order: WhatsApp platforms (2018 – 2022) → Pairly (Jul 2026 – present)

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WhatsApp platforms ›

Altur, Harambee and eButler on the WhatsApp Business API

Pairly ›

An AI-native operating system for founder-led teams

At a glance

Role
Lead engineer, Mmogo Media
When
2018 – 2022
Length
4 yr
Status
Delivered
Type
Platform, AI
Role
Founder / Lead Engineer
When
Jul 2026 – present
Length
4 mo so far
Status
In progress
Type
AI, Web, Platform

The problem

A WhatsApp-based job platform (Altur), Harambee Youth Accelerator's move from a mobisite to WhatsApp, and eButler's WhatsApp ordering with payments through Flash.
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

  • Python
  • Django
  • WhatsApp Business API
  • Infobip
  • Twilio
  • Python
  • FastAPI
  • PostgreSQL
  • Redis
  • Next.js
  • React
  • Azure
  • LangChain
  • Docker

Highlights

  • Conversational journeys on the WhatsApp Business API
  • Payments via Flash, Paystack and MTN MoMo
  • 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.

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