Hardac
The AI studio building vertical operating systems for real-world industries.
Hardac is the parent brand: the place where industry problems become AI-native software products.
About the founder →Hardac builds AI operating systems for real-world industries.
Our first product is the Landscape Construction OS: an AI operating system for high-ticket outdoor-living and landscape construction companies. It is being proven first inside Gillespie Landscape and built by Forge, Hardac’s AI agile development platform.
Live in production at Gillespie Landscape.
First proof
The Landscape Construction OS is already running inside the business it was built to understand.
The target is simple: prove that one owner can run a $10M landscape construction company with AI handling the operational layer. Every shipped system has to prove itself in live work first.
Live in production at Gillespie Landscape
Proposal cycle: weeks to hours
Estimating, proposals, and receipt reconciliation shipped
In flight: customer communication, project operations, finance operations
Planned: owner chief of staff
How Hardac is taking shape
Landscape OS
The first vertical product, built to prove that one owner can run a $10M landscape construction company with AI handling the operational layer.
Landscape OS connects proposals, schedules, field work, receipts, job costing, and owner briefing into one operating system.
Explore Landscape OS →Forge
The AI agile build engine behind Hardac.
Forge brings product, architecture, engineering, QA, documentation, and delivery agents into one accountable software team.
How Hardac builds →The Build Log is the proof trail: what has shipped, what is in flight, and what each workflow proves inside live operations.
See the Build Log →The problem
The bottleneck is not software. It’s operations.
Most owner-led service businesses do not break because they lack another dashboard. They break because the hard work lives between systems: estimates, follow-ups, scheduling decisions, field updates, receipts, job costing, customer context, and owner judgment.
Traditional software stores the work. People still run it.
Hardac builds AI systems that run the work.
First product
Landscape Construction OS
Hardac’s first vertical product is an AI operating system for high-ticket outdoor-living and landscape construction companies. It is not a one-off build for Gillespie Landscape. Gillespie is the operating lab where the system is proven against real estimates, crews, materials, customers, and project constraints.
The product starts with the proposal engine, expands into project management, finance operations, customer communication, and an owner chief of staff, then repeats those modules across similar companies. The goal is a durable operating model for the category, not another dashboard to manage.
How we build
Forge is Hardac’s AI agile development platform.
Hardac products are built by an AI agile development team: product, architecture, engineering, QA, documentation, and delivery agents working continuously with clear roles and accountability.
Product Owner
business context, user stories, acceptance criteria.
Architect
system design, data model, constraints.
Developer
implementation and integration.
QA
test plans, edge cases, regression checks.
Documentation
specs, runbooks, release notes.
Delivery Lead
scope, blockers, release rhythm.
Continuous rigorscope, acceptance criteria, QA, documentation, and deployment stay attached to the work.
The standard
We are not replacing rigor with speed.
We are using AI to make rigor continuous. Every change is scoped, checked against acceptance criteria, documented, tested, and prepared for deployment. The build log is part of the product, not an afterthought.
Build log
The wedge is proposals. The next module is projects.
The first wedge is the AI Proposal Engine. It turns customer context, design scope, pricing logic, and contract terms into signable proposals for large outdoor-living projects.
The next major module is an AI project manager that takes the sold pipeline, breaks projects into scopes, learns how long crews and subcontractors actually take, sequences the book of work, coordinates material timing, and re-sequences the schedule when weather, labor, or supplier delays change the plan.
That project manager is planned and in flight. The shipped systems are estimating, proposals, and receipt reconciliation.
Shipped
Shipped
AI Proposal Engine
Turns customer context, design scope, pricing logic, and contract terms into signable proposals for large outdoor-living projects. The proposal cycle has moved from weeks to hours.
Shipped
Receipt Reconciliation
Crews photograph receipts from the field; AI identifies the project, amount, and materials, then logs the expense to the right job.
In progress
In progress
Customer Communications Hub
Every call, text, and email tied to a customer, logged and searchable for traceability.
In progress
AI Project Manager
The next major module: sold work broken into scopes, crew timing learned, and schedules re-sequenced as reality changes.
In progress
Finance Operations
Job costs, reconciliations, and financial context kept closer to live operations.
In progress
Owner Chief of Staff
Calendar, notes, follow-ups, research, and operating context organized around the owner.
First proof
Gillespie is the operating laboratory
Gillespie Landscape is Hardac’s first customer, proving ground, and operating laboratory. The Landscape Construction OS is not being tested against a demo workflow. It is being tested inside a real landscape construction company with real proposals, customers, receipts, schedules, field updates, and owner decisions.
Every system that ships removes work from the business today and teaches Hardac what an AI-run landscape construction company needs to become tomorrow. The proof starts with Gillespie, but the operating model is built to repeat across the category.
Why it compounds
The operating context gets deeper with every workflow
Every workflow gives Hardac more operational context: how jobs are priced, how customers respond, how crews move, how materials are ordered, where margin leaks, when owners intervene, and which decisions actually drive the business.
That context becomes the foundation for AI operators that can take operational ownership across the company. The proposal engine teaches pricing and scope. Project operations teaches capacity and sequencing. Finance operations teaches where the work turns into margin.
Roadmap
From one company to an industry
Phase 1
Rebuild Gillespie system by system.
Run the Landscape Construction OS in production at Gillespie Landscape. Ship the proposal engine, harden reconciliation, and build the next operating modules against live work.
Phase 2
Repeat across the category.
Bring the same modules to similar landscape construction and outdoor-living companies. The product improves as common operating patterns become reusable system behavior.
Phase 3
Carry the model into adjacent industries.
Use the operating-system pattern, Forge build discipline, and field proof to build vertical AI operating systems for other real-world service industries.
Depth first. Repeatability second. Expansion only after the operating model proves itself in real work.
Who this is for
Investors, operators, and design partners
Hardac is built for people who understand that the next AI companies will own operations, not just automate tasks. We want investors who care about vertical depth, operators who know where real work gets stuck, and design partners who can help prove what an AI operating system should own.
Investor memo
Request the memo
Hardac is early, specific, and proof-led. If you are an investor, operator, or potential design partner who wants the deeper Landscape Construction OS thesis, request the investor memo.
Follow the build
Hardac is building the Landscape Construction OS in public enough to show the work. Leave your email for occasional updates as modules ship.
Building or operating in a real-world industry with heavy operational drag? → Contact