Bounded AI MVP Sprint
Support triage
Boundary matrix plus routing acceptance rubric. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
Workflow teardowns
Proof artifacts for AI workflows before they become projects.
A useful teardown names the operating claim, the action boundary, the evidence required, and the caveat. These examples show how Verdify turns common AI ideas into bounded workflows a team can inspect before a pilot becomes production risk.
Teardown table
The boundary is the center of the proof.
Each workflow separates what AI may do from what remains controlled by reviewers, systems of record, policies, or deterministic services.
| Workflow | Fit | Claim | Allowed AI role | Prohibited actions | Evidence required |
|---|---|---|---|---|---|
| Support triage | Software and AI-native | Reduce queue review time while improving escalation consistency. | Read tickets, classify urgency and topic, summarize history, draft internal notes, and recommend routing. | Auto-close tickets, send external responses, approve refunds, change account state, or override escalation policy. | Cycle time, reviewer acceptance rate, escalation quality, override rate, source trace completeness, and reopened-ticket rate. |
| Quality documentation | Life sciences, medtech, and high-trust operations | Assemble evidence packets faster without weakening required review. | Read approved source documents, extract evidence, draft summaries, flag missing records, and suggest document routing. | Sign records, approve quality decisions, alter controlled documents, invent source evidence, or bypass required reviewers. | Trace completeness, reviewer override rate, missing-source rate, cycle time, exception backlog, and audit-readiness defects. |
| CPG exception handling | Outdoor, natural products, and CPG | Shorten retailer, claims, return, or chargeback review without creating brand or claims risk. | Categorize exceptions, summarize retailer context, draft internal response options, and prepare evidence packets. | Invent product claims, send unapproved customer or retailer messages, approve deductions, or create fake review language. | Exception aging, acceptance rate, claims-review defects, deduction recovery notes, reviewer overrides, and response cycle time. |
| Field-service exception review | Cleantech and energy software | Help operators prioritize recurring field-service exceptions without overriding safety procedures. | Read alerts, work orders, telemetry summaries, service history, and customer context; draft triage notes and recommend next review steps. | Dispatch technicians without approval, override field procedures, silence safety alerts, change asset state, or make customer commitments. | Triage accuracy, false-recommendation rate, escalation timeliness, technician override rate, incident trace completeness, and backlog aging. |
| Supplier document review | Aerospace and advanced manufacturing | Compress first-pass supplier review while preserving traceability and authority. | Read supplier packets, extract required fields, compare against checklist criteria, flag missing evidence, and draft reviewer notes. | Approve supplier records, waive requirements, alter source files, bypass traceability, or issue external acceptance decisions. | Missing-field rate, source trace completeness, reviewer override rate, review cycle time, nonconformance linkage, and audit defect rate. |
| Engineering-change triage | Advanced manufacturing and software operations | Improve change-intake consistency without letting AI approve scope, priority, or release decisions. | Read change requests, classify impact area, summarize dependencies, suggest reviewers, and draft decision packets. | Approve changes, alter release plans, modify requirements, assign final priority, or bypass required engineering review. | Routing accuracy, dependency miss rate, reviewer override rate, cycle time to first review, rework rate, and decision trace completeness. |
Caveat: these are pattern teardowns, not performance claims. A real project still needs source-system review, stakeholder interviews, test cases, privacy review, telemetry design, and scorecard baselines.
How to use them
Turn a teardown into a controlled implementation path.
Verified AI Operations Audit
Quality documentation
Source trace register plus reviewer override log. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
AI Operations Scorecard
CPG exception handling
Claims boundary table plus exception scorecard. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
Verified AI Operations Audit
Field-service exception review
Alert taxonomy plus deterministic escalation rules. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
Bounded AI MVP Sprint
Supplier document review
Checklist extraction test set plus source evidence map. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
AI Operations Scorecard
Engineering-change triage
Change-impact rubric plus reviewer decision log. Start by validating prohibited actions, systems of record, approval paths, and scorecard metrics before building automation.
Scorecard pattern
The metrics change by workflow. The proof discipline does not.
Verdify does not treat a teardown as a case study or ROI claim. It is a planning artifact that identifies what would need to be measured before expansion.
Cycle time
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
Reviewer acceptance
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
Override rate
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
Trace completeness
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
Exception backlog
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
False recommendation rate
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
Business impact
Define the baseline, reviewer signal, failure mode, and evidence source before the workflow is scaled.
What a teardown is not
It is not permission for an agent to own the workflow.
It is not a claim that the workflow will improve before a baseline exists.
It is not a substitute for source-system review, test cases, privacy review, and operator acceptance.
Workflow teardowns are useful when the team has a real workflow to bound.
Good fit when
You can name the workflow owner and systems of record.
There are repeated decisions, drafts, reviews, or exceptions.
A wrong action would create customer, quality, brand, compliance, or financial risk.
You need evidence before approving a pilot or sprint.
Not a fit when
You want AI ideas without a specific workflow.
The team will not name prohibited actions.
There is no reviewer, authority layer, or rollback path.
You need production metrics before a baseline has been measured.
FAQ
Common buyer questions.
Are these client case studies?
No. These are reusable proof artifacts that show how Verdify decomposes common workflows before client-public case studies are available.
Why show prohibited actions so prominently?
For bounded AI operations, the prohibited actions are part of the design. They make authority, review, rollback, and scorecard needs explicit before anything is built.
How should a team use these teardowns?
Pick the closest workflow, list your systems of record and unacceptable actions, then use the Boundary Matrix or a Verified AI Operations Audit to turn the pattern into an implementation plan.