How we work
From one costly workflow to a measured production result.
We work beside the people who run the operation, agree the commercial terms before we build, and stay accountable after the system is live.
The engagement
Observe. Baseline. Deploy. Reconcile.
Every stage produces an artifact both teams can review. The process moves forward when the operating evidence is clear—not because a consulting calendar says it should.
01
Observe the operation.
Engineers sit with the people who run the workflow and document the systems, manual steps, decision points, and the operating constraint worth solving.
02
Set the baseline.
Both teams agree on the current cost or missed gross profit, the source data, the representative period, the value equation, and the exclusions.
03
Deploy the system.
We build inside the tools and controls already in use, with human approval wherever a consequential decision remains.
04
Reconcile the result.
We compare production evidence with the baseline, adjust for material outside changes, and share only verified first-year value: 80% to you, 20% to Monte Carlo.
Engagement blueprint
Responsibilities and decision rights are explicit.
What your team provides
The operators who know the work, access to the systems in scope, current policies, exceptions, and the decisions that must remain human.
What Monte Carlo owns
Workflow observation and modeling, system design, integration, production operation, exception handling, and continuous improvement of the deployed software.
Production evidence
Named source systems, action logs, recurring reports, workflow volumes, and the underlying data used to compare performance with the baseline.
Decision gate
Your team approves the baseline, production launch, consequential actions, value reconciliation, and any expansion into another workflow.
Measurement before implementation
Value is defined before software is built.
Your team and ours agree on the source systems, baseline, value equation, and exclusions before production. Results are reviewed from visible operating evidence—not a model-generated estimate.
Baseline
Representative period, workflow volume, current hours, costs, missed opportunities, and exclusions.
Value equation
The categories, inputs, and calculation both teams use to determine verified first-year value.
Evidence
Named source systems and recurring reports with the underlying operational data visible to both teams.
Attribution
Volume, mix, seasonality, and material outside changes are accounted for before value is assigned.
Capacity returned
Repetitive hours removed, multiplied by an agreed fully loaded labor rate. We report this as capacity returned—not automatic headcount reduction.
Operating cost avoided
Overtime, contractor spend, processing fees, rework, write-offs, or future hiring demonstrably avoided by the deployment.
Incremental gross profit
Additional contribution from higher throughput, faster response, recovered revenue, or fewer lost transactions—not undifferentiated topline revenue.
Illustrative example
If a deployment creates $500,000 in verified first-year value, Monte Carlo earns $100,000. Your company retains $400,000.
Capacity returned, operating cost avoided, and incremental gross profit are reconciled against one agreed equation. The same dollar is never counted twice.
Control remains with the customer.
Scoped, least-privilege credentials for every connected system.
No training on your data. Ever.
Humans approve money movement, supplier commitments, and irreversible system changes.
Action logs for every browser step, API call, generated artifact, and handoff.
Enterprise questions
The mechanics, stated plainly.
Start where the cost is easiest to see.
Establish the baseline