Michael Ward is a service-disabled Army veteran, AI systems builder, and founder of Varen AI, SWTMT Strategic Solutions, PlastiBioFuel, and Leading Valor Connections, based in Haslet, Texas. He designs and ships production AI systems across federal contracting, deep tech, govtech, cleantech, and quantitative trading.

What services does Michael Ward offer?

Custom software development, business and personal automation, custom bid engine creation for government contractors, and hands-on AI implementation and training for teams and owners.

How fast can you build?

Fast. The flagship land-intelligence platform on this site went from concept to a working product in six days — a scope most teams would staff for the better part of a year. Most engagements show working software inside the first week.

Where is Michael Ward located, and who do you work with?

Based in Haslet, Texas, in the Dallas–Fort Worth area — working remote-first with businesses and owners anywhere in the United States.

Email mward4102@gmail.com or use the contact form. The first consultation is free — bring the problem, leave with a plan.

Client Case Studies — Michael Ward | AI Systems Builder

01 — Square S Construction · Real Estate Development

She just talks to it.

The problem: Running a development means daily workflows, housing design, and city build requirements spread across disconnected tools — and the day disappears into them.

A custom AI system built for Square S Construction. Their superintendent runs full real estate development by conversation — daily workflows end to end, housing design for the Heritage Hills development, and city build requirements worked project by project. Delivered with a single walkthrough. No training program, no adoption curve.

Production AIReal Estate DevelopmentSingle Walkthrough

Case study card — AI estimating workflow, demolition. 30 minutes: full estimate runs, takeoff to priced output, down from hours of manual work. 90 percent: processing cost cut, standardized output, so accuracy went up while time went down.

02 — AI Estimating Workflow · Demolition

30-minute estimates. 90% lower cost.

The problem: Every estimate meant hours of manual takeoff and pricing — slow, expensive, and inconsistent from one run to the next.

An AI estimating workflow for a demolition contractor. Full estimate runs — takeoff to priced output — finish in 30 minutes, down from hours of manual work. Processing cost dropped 90%, and because the output is standardized, accuracy went up while the time went down.

EstimatingDocument ProcessingStandardized Output

03 — GLI · State & Municipal Bidding

Ask at 11. Bid by 1.

The problem: Municipal solicitations move fast, addenda land mid-bid with reject-if-missing language, and a full takeoff, estimate, and proposal takes days a small firm doesn't have.

Full project-specific municipal bids for GLI — takeoff, estimate, and proposal documentation built per solicitation, not from templates. One bid went from emailed request to finished package in under two hours, and a city addendum was worked into the submission the same afternoon. Alongside the bidding, a messy real estate portfolio was rebuilt overnight into a clean, properly valued, decision-ready document.

Municipal BiddingTakeoff & EstimatingPortfolio Valuation

04 — Safe Clean Solutions · Commercial Cleaning

Run NYC from Texas.

The problem: A New York City cleaning operation managed from Texas — invoices typed by hand, schedules juggled manually, client messages written one at a time, every day.

A full back-office automation for Safe Clean Solutions. One click generates the invoices, builds the schedule, and drafts the client communications, ready for review and send. The distance was never the problem; the manual work was. Those hours now go back into running the business.

Full AutomationInvoicing & SchedulingClient Comms

05 — PlastiBioFuel · Reactor Engineering

365 runs. Zero resets.

The problem: Reactor heat-recovery modeling only pays off if progress accumulates, and every AI session starts from a blank page.

A daily simulation loop built for PlastiBioFuel, my own cleantech company. Every day for over a year it has run a heat-recovery model on the reactor, scored the result against the full record of prior runs, adjusted, and logged what it learned. Each run is a function of every run before it. My oldest running system, and the proof behind the thesis every client build follows: managing context is the difference between answers and progress.

Persistent MemoryDaily SimulationRunning 1 Year+

In-house system card — PlastiBioFuel, reactor prototyping. Headline: Two AI engineers on the prototype. Zero new spend. Daily: full Monte Carlo simulations run against the current design and write results to a shared drive. Weekly: a scheduled AI cowork session reads the drive, updates the CAD model, and flags what the numbers say is wrong. Then: a human makes the call, and the design advances only when a change is clearly better.

06 — PlastiBioFuel · Reactor Prototyping

Two AI systems. One prototype.

The problem: Hardware iteration stalls when simulation, CAD, and design review sit in separate tools, and a prototype-stage company has no budget for another engineer.

A recursive design loop running on the PlastiBioFuel reactor. One system runs full Monte Carlo simulations against the current design every day and writes the results to a shared drive. A second scheduled session reads that drive weekly, updates the CAD model, proposes exterior changes, and flags what the numbers say is wrong. Two systems, one goal, each cycle starting from the strongest prior version so the design compounds instead of repeating itself. A human still makes the call on what advances. Built entirely on subscriptions already in use, with no new tooling spend.

Recursive LoopMonte Carlo & CADZero New Spend