Map the live work
Capture the inputs, systems touched, owners, exceptions, decisions, failure states, and recovery path before any automation is trusted.
Schubert Consulting LLC / Grand Rapids, Michigan
Own the command layer.
We build the private AI and automation systems that sit between people, tools, decisions, and risk. Not a GoDaddy template. Not a contractor pass-through. Not a chatbot wrapper. A company-owned control surface for the work that cannot drift.
In-house build
Human approvals
Evidence trails
Company-owned runbooks
The point is not to rent more hands. The point is to make the company harder to confuse, easier to operate, and safer to automate.
Control Room
A serious AI site should demonstrate control.
The website itself is now built around the operating model: visible boundaries, active system states, and a concrete path from messy workflow to controlled execution.
Interactive System Model
Approval-gated agent workflow
Agents prepare the move, operators approve the risk, and the company keeps the evidence.
Input
Messy work
calls, notes, portals, files
Agent
Prepare
summarize, compare, propose
Human
Approve
risk stays visible
Tools
Execute
CLI, API, app, browser
Proof
Evidence
logs, tests, handoffs
Mode: Gate
- Separate read-only review from production action.
- Require an operator decision before risky execution.
- Attach logs and recovery notes to the handoff.
Operating Stance
We do not farm out control.
If the system routes the work, touches the customer, changes the record, or spends company trust, it has to be owned. Schubert Consulting keeps strategy, build, QA, and handoff in the same accountable lane.
No. 01
No resource farming
No anonymous subcontractor chain between the company and the system that controls it.
No. 02
No black-box agents
Every action boundary is named: read, draft, approve, execute, rollback, escalate.
No. 03
No template thinking
The workflow decides the interface. The risk decides the gate. The operator decides the final move.
Method
From loose workflow to private command system.
Wrap the tools
Turn portals, APIs, files, dashboards, browser steps, and local software into commandable interfaces with readable state and repeatable output.
Install the gate
Agents can inspect, compare, summarize, draft, and stage. High-risk moves require an explicit approval path and a visible rollback plan.
Leave the company stronger
The deliverable is not just code. It is the map, registry, tests, logs, policy, operator runbook, and handoff that keep the system owned.
Artifacts
Evidence beats theater.
Great websites do not just look designed. They make the buyer understand what happens next. Great AI systems do the same.
Every command, app, API, credential boundary, allowed action, forbidden action, and owner in one place.
Clear rules for when an agent may inspect, draft, stage, execute, stop, or escalate.
A dashboard, CLI, agent file, or workflow screen that makes the system readable under pressure.
Logs, screenshots, validation output, decisions, open risks, and recovery notes kept with the work.
How the company runs the system, updates it, stops it, recovers it, and decides what gets automated next.
llms.txt, agents.json, and public-safe API descriptions that let assistants understand the company without exposing private data.
First Engagement
Start with one control point.
Bring the repeated workflow that currently depends on memory, screenshots, inbox archaeology, or one person who knows where everything is. The first sprint turns it into a controlled system boundary.
Day 1
Pressure map
What breaks, who owns it, which systems are touched, and where risk enters.
Days 2-3
Command prototype
A focused interface, harness, or agent workflow that proves the control model.
Days 4-5
Gate and handoff
Approval rules, tests, evidence packet, operator notes, and next build path.
Control Brief Builder
Define the system worth owning first.
Pick the pressure, boundary, and control mode. The brief updates into a precise starting point instead of a generic contact form.
Generated brief
Email this brief