How it works
From business case to outcome evidence.
Every engagement moves through the same disciplined sequence. Nothing ships on a hunch, and nothing consequential happens without a person.
Deterministic software for deterministic rules
If a step has a right answer — a calculation, a lookup, a routing rule, a validation — we write ordinary, testable software for it. It's predictable, cheap to run, and easy to verify.
AI for language, judgment, and ambiguity
We use AI where it genuinely helps — reading messy documents, drafting, summarizing, classifying, and researching — and we wrap it in checks. We don't use AI for decisions that should be deterministic.
The delivery sequence
Ten steps, every time.
Business case
We agree on the workflow, the problem it causes, and what a good outcome is worth. No build starts without a reason that holds up.
Workflow contract
We write down the inputs, steps, outputs, owners, and rules in plain language, so everyone agrees on what the system is responsible for.
Baseline
We measure the current workflow — time, volume, errors, and cost — so improvement is provable rather than asserted.
Risk classification
Each step is sorted into Green, Yellow, Red, or Black, so the system knows what it may do automatically and what needs a person.
Build
Deterministic software handles deterministic rules. AI is used for language, judgment, research, and ambiguity — never for decisions that should be deterministic.
Prompt evaluation
Where AI behavior repeats, we test it against fixed example sets — including held-out and adversarial cases — before it ships.
Full evaluation
We test the whole workflow end to end: integrations, edge cases, failures, and recovery — not just the happy path.
Shadow pilot
The system runs alongside your current process without taking action, so we can compare its output to reality before it touches anything.
Controlled release
We turn the system on gradually, with approval controls in place and a clear way to pause or roll back.
Outcome evidence
We report what ran, what people approved, what changed against the baseline, and what risks remain — in language you can act on.
When AI behavior repeats
We evaluate it before it ships — and keep evaluating.
Any repeated AI behavior is tested against fixed example sets, not judged by vibes.
- Development, holdout, and adversarial cases. Examples we build on, examples we hold back, and cases designed to break it.
- Protected datasets and budgets. Evaluation sets are locked, and token and cost budgets are enforced.
- Human-approved promotion. A change only goes live after it passes evaluation and a person approves it.
Human governance
A permission model on every consequential action.
Each step is classified so the system knows exactly what it may do on its own, what it should do and report, and what it must never do without a person.
- Green
Execute automatically
Low-risk, well-defined work runs without waiting for a person.
- Yellow
Execute and notify
The system acts, then tells a responsible person what it did.
- Red
Require explicit approval
A person must approve before anything happens. The default for consequential actions.
- Black
Prohibited
Off-limits actions the system will never take.
Red by default: Contracts, Payments, Purchases, Production deployments, Destructive actions, Access changes, Public claims, Sensitive communications. These require explicit human approval before anything happens.
This is an operational control model, not a certification. Mind to Method AI builds controlled systems; it does not provide legal, financial, medical, cybersecurity, or regulatory compliance guarantees.
See it applied to your workflow
A fixed-scope, $500 assessment of one workflow. You leave with a baseline, a design, and an implementation estimate. The fee may be credited toward an approved build under the written project agreement.