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Mo HelmyContact

Public-sector seller. Partner strategist. Coach. Builder of AI systems.

I help technology companies win government business, through partners and the sellers I develop.

Portrait of Mo HelmyI'm Mohamed Helmy, Mo to most people. I work on T-Mobile's national channel team, where I manage resale strategy and the relationships with the systems integrators and connected solutions providers who sell alongside us. Before that I spent years selling to cities, counties, school districts and state agencies, then ran the public-sector partner channel for the Southeast, which is where I learned how government really buys and who it trusts when it does.

On my own time I build agentic AI systems, and I'm particular about which model does which job. I work across the hosted models (Claude, OpenAI, Gemini, Meta's Muse, Kimi and GLM), run Qwen on my own hardware for the volume, use Flux, MiniMax, Wan and LTX for pictures, video and music, and use no model at all where the result has to repeat. That setup has produced a research tool that reads what a municipality publishes and shows what it is planning and funding, with the source on every line; the images, video, sound and animation pipeline behind an independent game studio; and an agent workflow I use every day, with its own checks, evals and judges, so nothing is called done until software says so. AI coding agents write most of the code. The architecture, the rules they work under and the call that the work is done are mine.

This site is for three readers. If you hire or run public-sector sellers, the work page is the record. If you run a partner program and are deciding whether to work with me, start with the marketplace case. If you build with AI agents and want to see how I make them prove their work, the applied AI page shows the systems and the receipts behind each one.

Recognition

“Mo is a strategic planner, does his research, and attacks the market like nobody else.”
Brett Olbrys, MPA, Executive Sales & Operations Leader. Managed Mo directly, December 1, 2022. Read all four.
of 91 in T-Mobile's partner channel
No. 1
2023 · every segment · Annual Winners Circle
new hires I mentored closed a first deal within three months
6 of 6
Channel team · all external hires · I sat in on every interview
other regions adopted the partner plan I wrote
2
Southeast public-sector partner channel · 2023
tests and a 16-scenario eval in my open-source agent harness
139
MIT licensed · 16 of 16 scenarios and 54 of 54 checks passing

Selected work

Three pieces of work I would point to first, with what was mine and what came of it.

A territory system that hit three times quota two years running

Here's a little background
New York, New Jersey and Delaware, three states that each buy their own way on their own calendar.
My contribution
I developed a system that started with the geography of the territory and segmented the value proposition by department, so a police department and a public works department each heard what was relevant to them. It also tracked how many people each department employed, which told us how much a conversation was worth and how to size it. One shared call plan kept reps and SDRs out of each other's way, and the public-sector database underneath it ended up in use by teams across the country.
Result
326% of quota in 2021, 380% in 2022, and 191 new customers. Other regions and SDRs still run the system.
Read the territory case, with the basis for each figure

The reseller marketplace business case leadership adopted

Here's a little background
Customers were signing a separate contract and getting a separate bill for every partner solution they bought.
My contribution
I wrote the business case: who the partners are, how the thing gets sold, and what gets built first.
Result
Leadership adopted it, and the product team is building it. T-Mobile talked about the marketplace publicly at AppDirect Thrive 2026.
Read the marketplace case

Municipal Intel: a municipality's public record, made readable

Independent work, built on my own time from public records.

Here's a little background
Municipalities publish a great deal: budgets, council minutes, meeting recordings, grant awards, announcements. Working out what a municipality is planning, what it is funding and what it is not, is an exhaustive research project, and almost nobody does it.
My contribution
The aim is to read that public record and show a municipality's strategic goals, what it funds and what it leaves unfunded, so anyone can learn what their own municipality is doing. I decide what it has to do, how it's built and what counts as done. AI coding agents write most of the code.
Result
1.9M+ funding records from 100+ sources, and 2,000+ municipalities seeded across seven states. A research tool, not a product.
Explore Municipal Intel: how it works and where AI belongs

How I develop people

I coach the individual and build the system around them, so what works for one person becomes something the whole team can run.

  • Find the constraint. Before I ask anyone to do more, I find out what is actually holding the number back: activity, targeting, conversion, deal size or cycle length. Then we fix that one thing first.
  • Coach to the person. On a new skill I go first, then step back as each person is ready. Feedback is specific: what happened, what they did, what it caused. Every coaching conversation ends with a goal and a next step.
  • Coach the deal. In deal reviews we walk each opportunity together, where it really stands and every step left to close it.
  • Build the system. When something works for one person, I turn it into a process the whole team can run. That is how the prospecting system ended up in other regions and on the SDR team.

I've managed people directly, a 13-person store team and a 12-person contract research team, and I've led the dealer and partner channels through influence.

See the leadership record

About

I live in Durham, North Carolina, with my wife, and I work remotely, with occasional travel. I speak English and Arabic. I came to the AI work from running operations, where most breakdowns happen at the hand-offs: an unclear contract, missing context, someone assuming someone else checked. When I started running coding agents on my own hardware I saw the same failures, loops, tests reported as passing, context lost between agents, so the guardrails, the verification and the receipts are where I put the work. The long version of the career is on the work page. The AI systems, the notes and the open-source harness have their own page. And if you need a bio or a photo, there's a press kit.

If you want to talk about public-sector growth, partner strategy or applied AI, I'd like to hear from you.

Email is the best way to reach me.