MW Michael WardAI Systems Builder Let's talk →
MW
Service-Disabled Veteran · Founder · Builder

I build AI that
wins contracts.

One builder with an unusually wide surface area — federal contracting, deep-tech R&D, govtech, cleantech, and quantitative trading. I teach myself the domain, then ship the system that solves it.

See what I've built → Start a conversation
Haslet, Texas· Founder of SWTMT Strategic Solutions· Building across 7+ verticals
Michael Ward, AI systems builder and founder of SWTMT Strategic Solutions

Most people pick a lane.
I build the whole road.

Chemistry, federal acquisition, market microstructure, insurance, govtech — different worlds, one method. Learn it deeply with AI, then orchestrate the build end-to-end and put it into production.

3
ventures founded & operated
7+
industries shipped in
24/7
automated bid pipeline running
No cap
on opportunities tracked at once
Selected Systems

Real builds. Real problems solved.

Every one of these is something I designed and built. Here's what each actually does — and three of them you can try right here.

Living RecordInteractive · sample
Ask the workspace
01 — Enterprise AI · Field Engineering

The Context Loop

The problem: Ops teams lose hours keeping status current across a dozen disconnected tools — and it's stale anyway.

A system that captures every event — emails, payment exceptions, recovery steps, onboarding — the moment it happens across nine tools, and writes it to a record that keeps itself current. The team stops updating status fields and just asks.

  • Live event capture across nine integrated tools
  • Self-maintaining records that are always current
  • Ask questions in plain English over live company state
  • Delivered with a full ROI model and per-seat pricing
OrchestrationIntegrationsRetrieval
Live Site
Arid Water Systems — go-to-market site for atmospheric water generators
02 — Cleantech · Go-To-Market

Water From Air

The problem: A hardware company pulling drinking water from air had to sell to homeowners, NGOs, and global field teams at once.

A full product and go-to-market site with a distinct track for each buyer, unit specs and financing, and an embedded AI assistant that answers questions and drives consultations.

  • Segmented architecture: Home · NGO · Global field
  • Unit specifications, financing, and NGO pricing
  • Embedded conversational AI assistant
  • Built to convert — every path leads to a booked call
Go-To-MarketHardwareConversational AI
Visit Arid Water Systems ↗
Case AnalyzerInteractive · sample

Veteran seeks an increased rating for tinnitus and service connection for PTSD. Service treatment records document acoustic trauma during a 2009 deployment. A 2021 C&P examination notes ongoing symptoms consistent with the claimed conditions.

03 — GovTech · Applied AI

Appeals Intelligence

The problem: Board of Veterans' Appeals cases bury reviewers in documents, and manual extraction is slow and hard to audit.

An AI workspace that classifies cases, extracts the issues and evidence, and attaches verifiable provenance to every output — so the work is fast and defensible.

  • Automated issue & evidence extraction
  • Case classification and routing
  • Verifiable provenance behind every output
  • Built for sensitive, regulated case files
Document AIAdjudicationProvenance
Longest-Running
PlastiBioFuel — converting PET plastic into renewable ethanol
04 — Deep-Tech · Biotech

PlastiBioFuel

The problem: PET plastic is everywhere, and most "biofuel" competes with the food supply.

My longest-running venture — and where this whole way of working started. An enzymatic platform that converts post-consumer PET plastic into ethanol. I taught myself the underlying chemistry with AI, used it to invent the process, then proved it out in a real university lab.

  • An original conversion process I invented — validated at lab scale, not theoretical
  • Successful depolymerization of post-consumer PET plastic
  • Independently replicated — repeatable results, not a one-off
  • Invited to submit a full proposal to the NSF SBIR program
EnzymaticPET → EthanolCleantechNSF SBIR
Visit the site ↗
Policy ReaderInteractive · sample

Drop in any insurance policy. The AI reads it and hands it back in plain English — with the gaps flagged.

▤ sample-dec-page.pdf dense · 2 pages
05 — InsurTech · Document AI

Policy, Understood in Seconds

The problem: Clients drown in policy paperwork; agents burn hours reading dec pages line by line.

Clients upload whatever they already have. AI reads it, builds a profile, compares coverage across carriers, and surfaces the gaps in plain language — behind a passwordless vault, with separate customer and agent experiences.

  • Reads dense policy documents and extracts every field with confidence scores
  • Compares coverage across carriers and flags the gaps
  • Magic-link, passwordless client vault
  • Dual customer / agent dashboards — transient by default
Document AIExtractionPasswordless
The Method

A loop for every problem — never the same one twice.

At the core of everything I build is an OODA loop — but it's never identical. I reshape it around each project's needs. The federal bid engine below is one instance of it running.

Example — the federal bid loop
O
Observe
Continuously ingest every open federal opportunity, around the clock.
O
Orient
Qualify each one for fit against the right entity and capability.
D
Decide
Prioritize what's worth pursuing — and what to pass on.
A
Act
Draft a compliant, submission-ready response in hours, not weeks.
↻  then it starts over, automatically.

This is the workflow I've been refining the longest — an OODA loop wrapped around a live pipeline, tracking pursuits across multiple companies simultaneously.

Continuous
opportunity ingestion, 24/7
Multi-entity
one pipeline, every company
Hours
from draft to submission-ready
Unlimited
pursuits tracked in parallel
🔒 The engine and the prompts stay in-house — what you see is the output.
The same four moves, re-tuned for each domain:
Bid loopR&D loopTrading loopTeaching loop
Also Running

Autonomous systems, working while I sleep.

Self-built trading and agent infrastructure — live data in, decisions and execution out, kept alive on their own hardware.

Live · trading

Quant tennis market-maker

A bot pricing live tennis markets on Kalshi, quoting both sides with full risk controls — trading live, now on its second iteration.

KalshiMarket-makingv2
Live · 24/7

Solana execution engine

A trading bot with its own signal generation and execution path, trading live around the clock under PM2 on dedicated hardware.

SolanaPM2Always-on
In development

AI-assisted equities desk

An Interactive Brokers system pairing live market data with AI-assisted decisioning. Fully architected and costed; moving into build.

IBKRArchitectedCosted
Teaching

I teach people to actually use AI.

My favorite thing isn't building for myself — it's sitting down with someone who runs a real business and showing them how to point AI at the problem that's been eating their week. Especially the folks the tech world forgets: non-technical owners and older entrepreneurs who were told this stuff isn't for them.

It is. I run hands-on sessions and small cohorts that start from a real problem you're facing and end with you able to solve it yourself — and when you'd rather have it just done, I'll build the implementation for you.

Bring this to your team →
  • 01
    Start from your real problem
    No toy demos — we work on the thing that's actually slowing you down.
  • 02
    Built for non-technical owners
    Plain language, no jargon, designed for people new to AI.
  • 03
    Walk away able to do it yourself
    You leave with a repeatable workflow, not a one-time trick.
  • 04
    Done-for-you when you want it
    Prefer it handled? I'll build and deploy the implementation.
About

A service-disabled veteran who builds across worlds most people would say don't belong on the same résumé.

SDVOSB BBA Finance · UNT Haslet, Texas Multi-entity founder

I'm Michael Ward. I build production AI systems across federal contracting, deep-tech, govtech, cleantech, and quantitative trading — usually by teaching myself the domain first and then orchestrating the entire build, from research to deployed, running code.

The throughline is a method, not an industry. I learn a problem deeply with AI until I can build in it, then I ship the result and put it in front of real users. That's how one person ends up with a biofuel process, a federal bid engine, and a live trading desk at once — and it's why there's almost no problem I'll call out of scope.

Contact

Have something hard to build?

Tell me what you're trying to solve. If it can be learned, designed, and shipped, I want to see it — the stranger and more ambitious, the better.

Or reach me directly mward4102@gmail.com