Workflows

Example workflows for Verified AI operations.

AI is most useful when the work is repeated, reviewable, and measurable. These examples show where an AI agent can help classify, summarize, route, draft, reconcile, or prepare evidence while humans and systems of record stay in control.

Start with an Audit Run the Risk Check

Examples

Repeated, reviewable, measurable work is the starting point.

These are examples, not the limit. If your workflow has repeated inputs, real operational consequences, and a way to measure outcomes, Verdify can audit whether an AI agent is a good fit and what controls need to exist before implementation.

Customer operations

Support ticket triage and escalation prep

High-volume queues slow down routing, escalation prep, and consistent handoffs.

Run a Sprint

AI may

AI may classify topic, urgency, account context, and draft an internal escalation brief.

AI may not

AI may not auto-close tickets, change account state, promise credits, or send customer-facing responses without approval.

Scorecard metrics

Cycle time, routing accuracy, reviewer acceptance, reopened-ticket rate, escalation quality, and customer-facing error rate.

Sales engineering and security

Security questionnaire and procurement response packets

Security questionnaires, architecture reviews, and procurement packets stall deals when answers and evidence are scattered.

Run a Sprint

AI may

AI may assemble approved responses, cite evidence, map unanswered questions to owners, and prepare a reviewable packet.

AI may not

AI may not send buyer-facing answers, invent security posture, approve legal language, or claim commitments without human approval.

Scorecard metrics

Response cycle time, source trace completeness, unsupported-answer rate, reviewer override rate, and deal-blocker aging.

Quality and operations

Quality documentation and evidence assembly

Reviewers lose time assembling source evidence, checking completeness, and turning records into reviewer-ready packets.

Start with an Audit

AI may

AI may read approved sources, extract evidence, flag missing records, draft summaries, and suggest routing.

AI may not

AI may not sign records, approve quality decisions, alter controlled documents, or invent source evidence.

Scorecard metrics

Trace completeness, missing-source rate, reviewer override rate, cycle time, exception backlog, and audit-readiness defects.

Life sciences and medtech

Medtech design-change / DHF evidence packs

Device teams lose time turning design changes into reviewable, traceable evidence.

Start with an Audit

AI may

AI may gather source documents, link requirements to tests, flag missing records, and draft a QA/RA review packet.

AI may not

AI may not approve changes, close CAPA, invent source evidence, alter controlled documents, release devices, or submit regulatory changes.

Scorecard metrics

Trace completeness, missing-source rate, reviewer override rate, packet cycle time, and unresolved-gap aging.

Service and implementation

Field-service exception triage

Telemetry, work orders, customer context, and technician notes are spread across systems before anyone can decide what needs action.

Build a Scorecard

AI may

AI may summarize alerts, group related events, classify likely cause, draft review notes, and recommend next-best action.

AI may not

AI may not override safety procedures, dispatch unsafe work, modify asset controls, or close incidents without owner review.

Scorecard metrics

False-recommendation rate, escalation timeliness, technician override rate, incident trace completeness, and backlog aging.

CPG, outdoor, and account operations

Retailer claims, chargebacks, and compliance packets

Chargebacks, returns, product claims, retailer portals, and customer exceptions create repeated review work with brand risk.

Start with an Audit

AI may

AI may categorize exceptions, summarize retailer context, draft internal response options, and prepare evidence packets.

AI may not

AI may not invent claims, create fake reviews, send unapproved messages, or make account commitments.

Scorecard metrics

Exception aging, response acceptance, claims-review defects, deduction recovery notes, reviewer overrides, and response cycle time.

Quality and supply chain

Recall-readiness traceability packets

Lot, shipment, co-manufacturer, and retailer records stay scattered until a mock recall becomes a fire drill.

Start with an Audit

AI may

AI may reconcile lot, shipment, co-manufacturer, retailer, and spreadsheet records, flag gaps, and draft recall-ready packets.

AI may not

AI may not initiate recalls, contact retailers, make public claims, approve disposition, or replace authorized recall owners.

Scorecard metrics

Trace completeness, missing-lot rate, reconciliation exceptions, drill cycle time, and owner override rate.

Cleantech and energy software

Cleantech pilot-to-procurement evidence packs

Strong pilots still die in procurement because results, assumptions, and diligence answers are not packaged for buyers.

Run a Sprint

AI may

AI may consolidate pilot KPIs, map claims to evidence, pre-fill diligence responses, and flag unsupported assertions.

AI may not

AI may not certify savings, sign contracts, claim customer endorsement, or overstate performance without human approval.

Scorecard metrics

Claim support rate, unsupported-assertion rate, diligence cycle time, reviewer override rate, and buyer-question aging.

Manufacturing and aerospace

NCR / MRB prep for manufacturing and aerospace

Quality teams waste engineering time gathering the same deviation evidence every time a nonconformance board meets.

Start with an Audit

AI may

AI may assemble deviation packets, highlight precedents, link source evidence, and prepare reviewer notes.

AI may not

AI may not decide disposition, waive requirements, approve supplier records, modify controlled records, or bypass board authority.

Scorecard metrics

Packet cycle time, source trace completeness, missing-evidence rate, board rework rate, and NCR recurrence.

Telemetry-heavy operations

Telemetry and sensor anomaly synthesis

Signals, logs, forecasts, incidents, tasks, and operating costs are visible but not converted into a supervised decision workflow.

Run a Sprint

AI may

AI may summarize telemetry, identify patterns, draft operating hypotheses, prepare handoff notes, and recommend controlled actions.

AI may not

AI may not actuate physical systems, change safety settings, or write into authoritative systems without control-layer checks and approval.

Scorecard metrics

Alert quality, action acceptance, stress or exception hours, cost impact, forecast error, override rate, and incident review findings.

Product and customer operations

Product feedback classification and routing

Feature requests, bug reports, customer context, and sales notes pile up without consistent classification or routing.

Start with an Audit

AI may

AI may classify feedback, summarize customer context, detect related themes, and prepare reviewer queues.

AI may not

AI may not update the roadmap, promise releases, change customer commitments, or suppress negative signals.

Scorecard metrics

Classification accuracy, routing acceptance, duplicate detection, cycle time, reviewer override rate, and missed-escalation rate.

Operations enablement

Internal knowledge and SOP retrieval workflows

Teams waste time finding current policy, SOP, technical, or process answers across scattered repositories.

Build a Scorecard

AI may

AI may retrieve approved sources, summarize current guidance, flag stale documents, and route unresolved questions to owners.

AI may not

AI may not create policy, override document control, expose restricted sources, or present stale guidance as authoritative.

Scorecard metrics

Answer acceptance, source freshness, stale-document rate, escalation rate, trace completeness, and reviewer override rate.

A workflow-first entry point is useful when the pain is already visible.

Good fit when

The workflow repeats often enough to define test cases.

AI can start by reading, classifying, drafting, recommending, or routing.

A reviewer, deterministic control path, or system of record remains authoritative.

The team can name at least one scorecard metric before implementation.

Not a fit when

The workflow is vague, one-off, or mostly political.

The team wants AI to take unrestricted action on day one.

Source evidence is unavailable or cannot be inspected.

No owner can approve exceptions or review scorecard results.

Bring your workflow

Not seeing your exact workflow?

Bring us one workflow, one backlog, or one operational pain point. We will help determine whether an AI agent belongs there, what controls need to exist, and how success should be measured.

Book a Fit Call Use the Control Matrix

FAQ

Common buyer questions.

How should these examples be read?

They are not a menu of industries or a checklist for buyers to self-diagnose. They show the operating patterns Verdify is built around: repeated review, drafting, routing, exception handling, telemetry synthesis, and evidence assembly where AI needs controls before it is trusted.

What makes a first workflow a good candidate?

A good first workflow has repeated volume, accessible source evidence, a clear reviewer or system of record, measurable outcomes, and action limits that can be written down before implementation.

What if the workflow needs direct execution?

Direct execution should be narrow, reversible, logged, and explicitly approved. Most first Verdify workflows start with read, classify, draft, recommend, and route actions before execution is considered.