PRD Example

Cloud Computer Vision - PRD

1. Executive Summary

One-sentence positioning

An automated image-auditing pipeline that uses computer vision to identify delivery risks, such as packages left in the rain or unsafe locations, and flags them for dispatcher review.

Problem We’re Solving

• The Core Problem: Shippers and dispatchers lack the resources to manually audit thousands of proof-of-delivery (POD) photos, leading to undetected compliance issues, weather-related damage, and theft risks.

• Who experiences it: Dispatchers, safety/compliance officers, and fleet owners.

• Why it matters now: Rising insurance claims and customer expectations for delivery quality require a proactive, rather than reactive, approach to delivery de-risking.

Proposed Solution (High Level)

• What we are building: An asynchronous computer vision pipeline that analyzes driver-uploaded photos for specific "risk indicators" (e.g., wet pavement, curb-side placement) using Azure AI Vision 4.0.

• What fundamentally changes: Auditing moves from a manual, needle-in-a-haystack search to an exception-based workflow where only high-risk deliveries are reviewed.

Primary Value Delivered

• Risk reduction: Prevent claims by detecting packages left in high-risk zones (rain, public streets).

• Operational efficiency: Automate the auditing process for 90%+ of standard deliveries.

• Strategic differentiation: Positions our platform as a "quality-first" logistics provider.

Who This Is For

• Buyer / Decision Maker: Fleet Owners, Operations VPs.

• Primary Users: Dispatchers and Audit/Compliance Teams.

• Secondary / Indirect Users: Drivers (via quality feedback) and Shippers (end-to-end transparency).

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2. Strategic Context & Alignment

2.1 Why Now

Advancements in Large Multimodal Models (LMMs) and Azure’s Image Analysis 4.0 allow for "dense captions" and "read" features that can now understand complex scene contexts (e.g., "package on a wet porch") with high confidence. This allows us to advance our AI-first, autonomous TMS vision, via APIs and without significant AI/ML resource investment.

2.2 Strategic Alignment

• Marketplace Strategy: This serves as a premium "upsell" add-on for high-value cargo shippers (i.e. Medical and Pharma).

• Platform Vision: Moves our application from a simple tracking tool to an intelligent enforcement engine.

2.3 Non-Goals (Explicit)

What this project is intentionally not doing

• Real-time in-app coaching for drivers during the photo-taking process (this is handled by our “Edge Computer Vision” with Captur.ai).

• Automated claim filing or driver pay/settlement adjustments.

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3. Problem Statement & Opportunity

3.1 Current State

Today, photos are simply stored as static blobs. Unless a customer reports a missing or damaged package, these images are never viewed. If a package is left in the rain, it may be hours before the dispatcher or customer realizes the error.

3.2 Impact

• Business Impact: High claim costs and potential churn of premium shippers.

• User Impact: Dispatchers are overwhelmed by data they cannot process.

3.3 Opportunity

By using the "Dense Captions" and "Tags" features of Azure AI Vision, we can programmatically "see" risks. For example, detecting "wet" or "puddle" keywords in a scene description can trigger an immediate alert to a dispatcher to contact the customer or driver.

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4. User Experience: End-to-End Journey

4.1 Feature Discovery

Where users encounter this

• Where: Visual Dispatch Board: Map (driver and stop cards) and Grids (shipments table, POD column). Shipments Form (Attachments section). In-app Marketplace under "AI & Compliance Tools."

• Contextual Prompt / Upsell Motions: Suggested to users in the "Enforcements and Actions" section via “Computer Vision” Enforcement/Label (disabled). Suggested to users on the Shipments form in Attachments section (info text)

What users see

• Value proposition

• Who it’s for

• Any prerequisites or limitations

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4.2 Activation & Access Control

• Permissions: Dispatchers and Drivers can view. Only Admins can activate the feature in the Marketplace.

• How: Enabled via Marketplace card. Once active, users can apply the “AI Vision Audit" label to specific Customers, Drivers, Orders, etc. via “Labels” (same as other ‘Actions’ today).

• Key Decisions: Users define the "Risk Threshold" (e.g., "Only flag photos with <70% confidence of residential context").

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4.3 Post-Activation Experience (Steady State)

Day-to-Day UX Changes

• What looks different in the UI

• New indicators, states, or behaviors

• New constraints or automation

Failure & Edge States

• What happens when something goes wrong

• How users are informed

• How issues are resolved

User Confidence Signals

• How users know the system is “working”

• Visual indicators, logs, or explanations

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5. Scope & Phasing

Phase 1 — MVP / Initial Release

• Core capabilities

• Explicit exclusions

• Assumptions

Phase 2 — Future Enhancements

• Expanded workflows

• Deeper automation or intelligence

• Cross-product or bidirectional integrations

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6. Functional Requirements (High Level)

• Core behaviors

• Required automations

• Gating or enforcement logic

• Lifecycle management

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7. System Architecture & Technical Considerations

7.1 Systems Involved

• Internal systems

• External systems / partners

• Source of truth for each domain

7.2 Data Ownership & Flow

• What data is read vs written

• Sync directionality

• Real-time vs async behavior

7.3 Key Technical Decisions

• APIs vs events

• Webhooks vs polling

• Standardization vs configurability

7.4 Known Technical Risks

• Legacy data

• Identity matching

• Performance or reliability concerns

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8. Data Model & Schema Strategy

Standardized Fields

• Core attributes required

Extensibility

• What remains configurable

• How customers retain flexibility

Explicit Exclusions

• Sensitive or regulated data we do not store

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9. Monetization, Packaging & Enablement

9.1 Pricing & Packaging

• Add-on vs tier-gated

• Trial behavior

• Usage considerations

9.2 Sales & Support Enablement

• How Sales positions this

• What Support can / cannot override

• Documentation requirements

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10. Success Metrics & Guardrails

Primary Success Metrics

• Adoption

• Revenue or risk reduction

• Time-to-value

Operational Guardrails

• Failure rates

• Support impact

• User friction indicators

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11. Risks, Tradeoffs & Open Questions

Open Product Decisions

• Behavior toggles

• UX tradeoffs

• Enforcement vs flexibility

Business Risks

• Customer pushback

• Sales friction

• Over-constraint of workflows

Technical Risks

• Migration complexity

• Scale or latency issues

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12. Delivery Plan & Epics

Primary Epic(s)

• Jira links

Dependencies

• Other teams

• External partners

• Platform work

Target Milestones

• MVP

• Beta

• GA

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13. Appendix & Links

• Discovery Brief

• Design Brief

• Figma

• Jira Epics

• Marketplace listing

• Enablement docs

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PRD Quality Bar (Checklist)

Before marking this PRD “Ready”: