Open almost any app with a stats screen and you hit the same miss. It counts something, then leaves you to work out why you should care. "400 hours" and "400 hours reclaimed" are the same number. One reads as a metric. The other is a small win the user gets to own. Most products stop at the count. Sessions, streaks, totals, all true and all forgettable. Do that work for them. Say what the number bought them. Time back, progress made, a habit they're keeping. Leave the user to supply the meaning and most won't. They close the screen, and the one thing that might pull them back tomorrow closes with it.

This is so true. A number means a lot more when you show the actual win behind it.

AgentFlow10 — AI Agent & Automation Platform

Lead Engineer · Self-hosted AI agent platform with a visual workflow builder

The problem

Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.

There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.

What I built

AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.

The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.

What I did

I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.

Notable pieces of engineering:

A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent

LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text

A self-hosting path that a non-specialist can actually deploy

Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows

Why it matters for clients

This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.

Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker