A person owns the decision
AI may extract, classify, draft, and recommend. Sensitive actions remain visible and approval-gated.
Supervised AI operations
Build useful AI.
Keep humans in control.
LLMMoat explores how small businesses can use AI without surrendering judgment, security, or accountability. We build practical workflows, test them honestly, and document what works.
The moat
AI is easy to demo. Reliable operations are harder. Our work focuses on the controls around the model—the part that determines whether a workflow is trustworthy in practice.
Access has a boundary
Dedicated credentials, least privilege, protected secrets, and deliberate shutdown procedures.
Claims require proof
Real tests, observable results, and direct reporting when an experiment fails or remains uncertain.
How we work
From workflow to controlled system.
The model is one component—not the operating system. We combine deterministic automation, narrowly scoped AI assistance, human approval, and audit-ready records.
Observe
Document the current process, exceptions, risks, and human responsibilities.
Design
Choose where rules are enough and where AI adds measurable value.
Validate
Test normal work, bad inputs, outages, reversals, and security controls.
Operate
Deploy with monitoring, approvals, ownership, and a manual fallback.
Work in public
Experiments, not theater.
We document supervised AI projects as evidence: what we tried, where judgment stayed human, what the system cost, and what the results actually showed.
Small-business operations lab
A fictional-company environment for testing invoice intake, extraction, approvals, audit controls, and recovery without exposing real customer data.
IN DEVELOPMENTMila Rhodes experiment
A transparent AI-creator project testing identity consistency, responsible disclosure, production efficiency, and measurable audience response.
ACTIVE CASE STUDYStart with the process.
Not the hype.
Tell us which repetitive workflow is costing time, dropping leads, or creating avoidable errors.