---
project_name: "AI-Assisted Product Categorization System"
user_name: "Sayre"
date: "2026-05-12"
status: "draft"
version: "1.0"
stepsCompleted: ["step-01-init", "step-02-discovery", "step-02b-vision", "step-02c-executive-summary", "step-03-success", "step-04-journeys", "step-05-domain", "step-06-innovation", "step-07-project-type", "step-08-scoping", "step-09-functional", "step-10-nonfunctional", "step-11-polish", "step-12-complete"]
releaseMode: single-release
inputDocuments: ["/var/www/html/ai_cats/_bmad-output/project-context.md"]
documentCounts:
  briefs: 0
  research: 0
  brainstorming: 0
  projectDocs: 1
classification:
  projectType: "web_app"
  domain: "data_management"
  complexity: "medium-high"
  projectContext: "brownfield"
---

# Product Requirements Document (PRD)
## AI-Assisted Product Categorization System

---

## Document Information

| Field | Value |
|-------|-------|
| **Project Name** | AI-Assisted Product Categorization System |
| **Version** | 1.0 |
| **Status** | Draft |
| **Date Created** | 2026-05-12 |
| **Author** | Sayre |
| **Last Updated** | 2026-05-12 |

---

## Executive Summary

The AI-Assisted Product Categorization System addresses the critical gap between AI automation and enterprise governance in Product Information Management (PIM) and Master Data Management (MDM) environments. Data stewards and product managers struggle with manual product categorization at scale while facing regulatory requirements for auditability, traceability, and human oversight of automated decisions. This system provides AI-powered categorization with confidence-based decisioning, explainable evidence, and seamless integration with Akeneo Collaborative Workflow for human review of ambiguous cases.

The system imports supplier/product data from CSV files, normalizes products and attributes into PostgreSQL, runs categorization logic using existing enrichment data and category rules, stores predictions with confidence scores and evidence, and routes decisions below the 90% confidence threshold to Akeneo Collaborative Workflow for formal approval. All decisions are fully auditable with traceable import jobs, predictions, workflow submissions, and approval outcomes. The system is self-hosted to maintain data sovereignty and avoid unapproved cloud dependencies.

### What Makes This Special

The differentiator is not the AI categorization technology itself, but the enterprise-grade governance wrapper around it. Unlike generic AI categorization tools that pitch "set it and forget it" automation, this system treats AI as an augmentation tool with guardrails embedded in established PIM governance processes. Confidence thresholds (starting at 90%), explainable evidence for every prediction, and integration with existing Akeneo Collaborative Workflow rather than replacing it address the specific needs of regulated data environments where data stewards require trust, auditability, and human oversight.

The governance-first approach solves the core insight that AI categorization without governance is dangerous in enterprise PIM environments. By preserving original supplier values for traceability, maintaining comprehensive audit trails, and routing ambiguous cases to established approval workflows, the system enables data stewards to trust AI recommendations while maintaining operational control and regulatory compliance.

## Project Classification

**Project Type:** Web Application with operational dashboard for data importing/processing and review workflows

**Domain:** Data Management (PIM/MDM, B2B eCommerce product data enrichment)

**Complexity:** Medium-High (involves ML components, data governance, and integration with external Akeneo PIM system)

**Project Context:** Brownfield (building upon existing project context and established requirements)

---

## Success Criteria

### User Success

Data stewards and product managers achieve confidence and efficiency when the system provides explainable evidence and reasoning behind categorization recommendations. Success is realized when confidence levels increase over time and fewer items require manual review in Akeneo Collaborative Workflow. Users experience reduced cognitive load through clear evidence presentation and reliable high-confidence predictions that can be trusted.

### Business Success

**3-Month Targets:**
- 50% reduction in time to categorize products compared to manual processes
- 85% accuracy on high-confidence (90%+) categorization cases
- 60% reduction in manual review volume through confidence-based routing
- Measurable improvement in data quality and consistency across product catalog

### Technical Success

- Reliable Akeneo PIM integration for bidirectional data flow (Akeneo → classification → Akeneo workflow)
- Explainable evidence generation for every prediction with clear reasoning
- Confidence scoring mechanism that improves over time through feedback
- Supplier enrichment data import via CSV for enhancing categorization accuracy
- Self-hosted deployment maintaining data sovereignty and avoiding cloud dependencies

### Measurable Outcomes

- Percentage of products auto-categorized vs. requiring human review
- Average time per product categorization (baseline vs. after system deployment)
- Confidence score distribution across categorized products
- Akeneo workflow approval/rejection rates for AI-suggested categories
- Error rate reduction compared to manual categorization baseline

## Product Scope

### MVP - Minimum Viable Product

- Akeneo PIM integration for product data ingestion and classification result submission
- AI-powered categorization with confidence scoring (90% threshold for auto-approval)
- Explainable evidence generation showing reasoning behind each prediction
- Supplier enrichment data import via CSV files
- Basic web dashboard for monitoring categorization status and confidence metrics
- Audit trail for all categorization decisions and Akeneo workflow submissions

### Growth Features (Post-MVP)

- Machine learning model retraining based on Akeneo workflow feedback
- Advanced analytics dashboard with trend analysis and confidence score evolution
- Bulk categorization capabilities for large product datasets
- Custom confidence threshold configuration per category or product type
- Integration with additional PIM systems beyond Akeneo

### Vision (Future)

- Multi-language support for global product catalogs
- Real-time categorization streaming for continuous product updates
- Advanced ML models incorporating image recognition for product categorization
- Marketplace of pre-trained categorization models for different industry verticals
- Predictive analytics for category trends and product classification optimization

---

## User Journeys

### Data Steward Journey - Sarah

**Opening Scene:** Sarah is a data steward staring at a spreadsheet of 500 new SKUs from a supplier. She's been at this for 4 hours and has only categorized 50 products. She doesn't know what these products are - the part numbers are foreign, the manufacturer categories are vague, and she's making educated guesses that she knows are probably wrong. Her manager is asking when this batch will be done, and she's feeling overwhelmed and incompetent.

**Rising Action:** Sarah opens Akeneo and sees the new "AI Categorization" option. She selects the 500 SKUs and submits them for classification. The system pulls in enrichment data, analyzes part number patterns, cross-references manufacturer categories, and examines product descriptions. Within minutes, she sees results: 380 SKUs have 90%+ confidence with detailed reasoning explaining why each category was suggested. The remaining 120 have lower confidence but still include evidence and suggested categories.

**Climax:** Sarah reviews the high-confidence batch first. The reasoning is clear - "Part number pattern ABC-123-X typically indicates industrial fasteners in category X" - and she can see the evidence trail. She bulk-approves the 380 confident recommendations. For the lower-confidence items, she uses the AI suggestions as a starting point. Instead of researching from scratch, she validates the suggested category with a quick check, finding most are correct or close enough to easily adjust.

**Resolution:** What took her 4 hours for 50 products now takes 30 minutes for 500. She feels competent and efficient. The AI doesn't replace her judgment, but it gives her a knowledgeable assistant that understands the products better than she does. Her confidence grows as she learns from the AI's reasoning patterns.

### System Admin Journey - Sayre

**Opening Scene:** Sayre needs to deploy the categorization system. He's concerned about maintaining data sovereignty - no cloud AI services, everything self-hosted. He's worried about model updates, monitoring system health, and ensuring the Akeneo integration doesn't break during updates.

**Rising Action:** Sayre accesses the admin dashboard to configure the system. He sets up the Akeneo API credentials, configures confidence thresholds, and uploads supplier enrichment data via CSV. The system provides clear health indicators - API connection status, model version, recent categorization statistics, and error logs. He runs a test batch of 10 SKUs to verify the round-trip: Akeneo → classification system → back to Akeneo workflow.

**Climax:** The test succeeds perfectly. Sayre sees the categorization results appear in Akeneo with confidence scores and evidence. He reviews the admin dashboard's monitoring features - CPU usage, memory, prediction latency, and error rates. Everything looks stable. He schedules automated backups and sets up alerts for any integration failures.

**Resolution:** Sayre feels confident in the system's reliability. The self-hosted architecture gives him full control, and the monitoring tools provide visibility into system health. He can deploy updates without worrying about breaking the Akeneo integration, and the audit logs give him traceability for any compliance requirements.

### Support/Integration Journey - Sayre

**Opening Scene:** A data steward reports that a batch of categorizations failed. Sayre needs to troubleshoot whether it's an Akeneo API issue, a data quality problem, or a system error. He's wearing his support hat and needs to investigate quickly.

**Rising Action:** Sayre checks the admin dashboard's error logs. He sees the failed batch with specific error messages - "Akeneo API timeout during result submission." He checks the Akeneo connection status and sees intermittent connectivity issues. He reviews the audit trail to see which SKUs were successfully categorized before the failure and which are stuck in processing.

**Climax:** Sayre identifies the root cause - Akeneo's API rate limiting during peak hours. He adjusts the batch size and retry logic in the integration configuration. He manually resubmits the failed batch, and this time it succeeds. He documents the rate limiting issue and the configuration change for future reference.

**Resolution:** The data steward's issue is resolved, and Sayre has improved the system's resilience. The audit logs and error tracking made troubleshooting straightforward. He adds a monitoring alert for API rate limits to catch this issue proactively in the future.

### Journey Requirements Summary

These journeys reveal capabilities for:

**Data Steward Capabilities:**
- Bulk categorization submission from Akeneo
- Confidence-based review interface
- Explainable evidence display with reasoning trails
- Bulk approval workflows for high-confidence predictions
- Category adjustment tools for low-confidence items
- Learning from AI reasoning patterns over time

**System Admin Capabilities:**
- Configuration dashboard for system settings
- Akeneo API credential management and testing
- CSV enrichment data import and validation
- Health monitoring with real-time system metrics
- Automated backup and recovery procedures
- Alert system for integration failures and system issues

**Support/Integration Capabilities:**
- Comprehensive error logging and audit trails
- Batch retry mechanisms for failed categorizations
- API monitoring and rate limiting detection
- Troubleshooting tools with detailed error context
- Configuration adjustments without system restart
- Documentation of common issues and resolutions

---

## Innovation & Novel Patterns

### Detected Innovation Areas

**Micro-Review Interfaces:** Sub-second human validation through optimized UI patterns that transform categorization review from minutes to seconds per item. Instead of traditional detailed review forms, presents streamlined decision interfaces with one-click approve/adjust workflows, pre-loaded with AI reasoning and evidence.

**Continuous Learning:** Real-time model updates from each Akeneo workflow decision rather than periodic batch retraining. Every approval or rejection immediately feeds back into the model, creating a self-improving system that adapts to new product patterns and category rules without requiring manual model retraining cycles.

**Counterfactual Evidence:** Showing what would change the AI's decision, not just why it made it. Instead of static reasoning like "Part number pattern indicates category X," displays dynamic counterfactuals like "This would be category Y if the manufacturer category were different" or "Confidence would increase to 95% if supplier enrichment data included field Z."

**Multi-Dimensional Confidence:** Confidence scoring across multiple dimensions rather than a single percentage. Separate confidence scores for category accuracy, data quality, supplier reliability, and pattern matching strength. This provides richer governance signals and helps reviewers understand which dimensions need attention.

### Market Context & Competitive Landscape

Most AI categorization tools focus on accuracy metrics and black-box predictions. The governance-first approach with explainable evidence is differentiating in the PIM/MDM space. The innovative angles above - particularly continuous learning and counterfactual evidence - represent advances beyond current market offerings, which typically use batch model updates and static reasoning explanations.

### Validation Approach

**Micro-Review Interfaces:** A/B test against traditional review forms, measuring time-per-decision and error rates.

**Continuous Learning:** Start with read-only feedback collection, measure prediction improvement over time, then enable live model updates once validated.

**Counterfactual Evidence:** User testing with data stewards to measure if counterfactuals improve decision accuracy and confidence compared to static reasoning.

**Multi-Dimensional Confidence:** Analyze historical categorization data to determine if multi-dimensional scores better predict human approval rates than single confidence scores.

### Risk Mitigation

**Continuous Learning:** Implement confidence bounds on real-time updates to prevent model drift, maintain snapshot capability for rollback.

**Counterfactual Evidence:** Ensure counterfactuals are computationally efficient and don't significantly increase prediction latency.

**Multi-Dimensional Confidence:** Start with single confidence score, add dimensions incrementally based on data availability and validation results.

---

## Web Application Specific Requirements

### Project-Type Overview

Multi-Page Application (MPA) with flexibility to adopt Single Page Application (SPA) patterns where beneficial. PHP-first architecture with server-rendered pages for core functionality, JavaScript for interactive elements. Internal operational tool accessed through Akeneo integration, focused on data steward workflows for product categorization review and system administration.

### Technical Architecture Considerations

**Browser Support:** Modern browsers only - Chrome 90+, Firefox 88+, Safari 14+, Edge 90+. No legacy browser support required, enabling use of modern JavaScript features and CSS capabilities.

**Application Architecture:** Server-side rendering with PHP for core functionality, JavaScript for interactive dashboard elements and AJAX for status updates. Can adopt SPA patterns for specific interfaces (e.g., admin dashboard) where user experience benefits justify the complexity.

**Performance Targets:** Batch categorization processing at 5-10 seconds per SKU (100 SKUs = 500-1000 seconds total). Dashboard refresh intervals of 5-10 seconds for status updates. Page load times under 2 seconds for dashboard and review interfaces.

**SEO Strategy:** Not applicable - internal tool accessed via Akeneo integration, no public-facing components requiring search engine optimization.

**Accessibility Level:** No specific WCAG compliance requirements. Basic usability considerations (keyboard navigation, clear visual hierarchy) sufficient for internal enterprise use.

### Implementation Considerations

**Responsive Design:** Desktop and tablet viewports only for dashboard and review interfaces. No mobile device support required - tool will not be accessible on mobile devices.

**JavaScript Framework:** Plain JavaScript preferred over frameworks unless specific SPA patterns justify framework adoption. Focus on progressive enhancement - core categorization submission works via form POST without JavaScript, dashboard becomes static without JavaScript.

**State Management:** Server-side session management for user authentication and authorization. Client-side state limited to UI interactions (form validation, status polling, dynamic content loading).

**Caching Strategy:** No server-side caching due to high data volume. Browser caching for static assets. No complex client-side state management required.

**Error Handling:** Recovery paths defined per error type during implementation. AJAX request failures include clear error messages with user notification after retry attempts. Graceful degradation when JavaScript fails - core categorization submission functions via server-side rendering.

**Polling Strategy:** Status updates via AJAX polling at 5-10 second intervals during batch processing. Consider exponential backoff for long-running jobs to reduce visual noise while maintaining responsiveness.

---

## Project Scoping

### Strategy & Philosophy

**Approach:** Single release with must-have prioritization due to time pressure. Focus on delivering core value proposition - AI-assisted categorization with governance wrapper - while deferring advanced analytics and sophisticated ML features to future iterations.

**Resource Requirements:** Single developer (Sayre) with PHP, PostgreSQL, and Python skills. No specialized ML team required for initial implementation. Self-hosted deployment eliminates cloud infrastructure complexity.

### Complete Feature Set

**Core User Journeys Supported:**
- Data steward categorization review and approval workflow
- System admin configuration and monitoring
- Support/integration troubleshooting and maintenance

**Must-Have Capabilities:**
- Akeneo PIM integration for bidirectional data flow (Akeneo → classification → Akeneo workflow)
- AI-powered categorization with confidence scoring (90% threshold for auto-approval)
- Explainable evidence generation showing reasoning behind predictions
- Supplier enrichment data import via CSV files
- Basic web dashboard for categorization status and confidence metrics
- Audit trail for all categorization decisions and Akeneo workflow submissions
- Admin configuration dashboard (API credentials, confidence thresholds)
- Error logging and batch retry mechanisms for failed categorizations
- Confidence-based routing to Akeneo Collaborative Workflow
- Server-side session management and authentication

**Nice-to-Have Capabilities:**
- Advanced analytics dashboard with trend analysis and confidence score evolution
- Multi-dimensional confidence scoring (start with single confidence score)
- Counterfactual evidence (start with static reasoning only)
- Continuous learning from workflow feedback (start with periodic model retraining)
- Micro-review interfaces for sub-second validation (start with standard review forms)
- Custom confidence threshold configuration per category or product type
- Bulk categorization capabilities for large datasets
- Exponential backoff for status polling (fixed 5-10 second intervals acceptable)

### Risk Mitigation Strategy

**Technical Risks:** Akeneo API integration complexity and ML model accuracy may not meet targets
- Mitigation: Early API testing with small batches, fallback to rule-based categorization if ML underperforms, conservative initial confidence thresholds

**Market Risks:** Data stewards may not trust AI recommendations without extensive validation
- Mitigation: Focus on explainable evidence quality, ensure confidence thresholds are conservative initially, provide clear audit trails for compliance

**Resource Risks:** Time pressure may force compromises on feature completeness
- Mitigation: Prioritize core integration and basic evidence generation, defer advanced analytics and sophisticated ML features, maintain flexibility to adjust scope based on implementation progress

---

## Functional Requirements

### Product Categorization

- FR1: Data stewards can submit products from Akeneo for AI-powered categorization
- FR2: System can categorize products using multiple data sources (part number patterns, manufacturer categories, enrichment data, product descriptions, specifications)
- FR3: System can generate confidence scores for each categorization prediction
- FR4: System can route predictions below 90% confidence to Akeneo Collaborative Workflow for human review
- FR5: System can auto-approve predictions at or above 90% confidence threshold
- FR6: Data stewards can review categorization predictions with explainable evidence
- FR7: Data stewards can approve or reject AI-suggested categories in Akeneo workflow
- FR8: Data stewards can adjust categories for low-confidence predictions
- FR9: System can process categorization in batches with status tracking

### Evidence & Confidence

- FR10: System can generate explainable evidence showing reasoning behind each prediction
- FR11: System can display evidence trails linking predictions to source data
- FR12: System can track confidence score distribution across categorized products
- FR13: System can maintain audit trail of all categorization decisions and evidence

### Akeneo Integration

- FR14: System can receive product data from Akeneo via API
- FR15: System can submit categorization results back to Akeneo via API
- FR16: System can integrate with Akeneo Collaborative Workflow for human review
- FR17: System can track Akeneo workflow approval and rejection outcomes
- FR18: System can handle Akeneo API rate limiting and retry logic

### Data Management

- FR19: System can import supplier enrichment data via CSV files
- FR20: System can validate and normalize imported enrichment data
- FR21: System can store product and attribute data in PostgreSQL
- FR22: System can preserve original supplier values for traceability
- FR23: System can handle SKU/MPN/part numbers as strings without numeric coercion

### System Administration

- FR24: System administrators can configure Akeneo API credentials
- FR25: System administrators can configure confidence threshold settings
- FR26: System can provide configuration dashboard for system settings
- FR27: System can manage user authentication and session management
- FR28: System can schedule automated backups of system data

### Monitoring & Troubleshooting

- FR29: System can log all categorization decisions with timestamps
- FR30: System can log errors and failures with detailed context
- FR31: System can provide health monitoring dashboard with system metrics
- FR32: System can retry failed categorization batches automatically
- FR33: System can provide troubleshooting tools for error investigation
- FR34: System can send alerts for integration failures and system issues
- FR35: System can display categorization status and progress to users

---

## Non-Functional Requirements

### Performance

- NFR1: Dashboard pages load within 2 seconds on modern browsers
- NFR2: Batch categorization processes 5-10 seconds per SKU (100 SKUs = 500-1000 seconds total)
- NFR3: Status polling updates display within 5-10 seconds during batch processing
- NFR4: CSV enrichment data import completes within 30 seconds for files up to 10,000 rows

### Security

- NFR5: Akeneo API credentials stored in environment variables, never in code or version control
- NFR6: Admin dashboard requires authentication for access
- NFR7: All data transmitted to/from Akeneo via HTTPS
- NFR8: PostgreSQL database access restricted to application credentials only
- NFR9: System logs do not expose sensitive data (API credentials, personal information)

### Integration

- NFR10: Akeneo API calls timeout after 30 seconds with retry logic
- NFR11: System handles Akeneo API rate limiting with exponential backoff
- NFR12: API data format compatibility maintained with Akeneo version changes
- NFR13: Failed API integrations trigger alerts within 5 minutes

### Reliability

- NFR14: System maintains 99% uptime during business hours
- NFR15: Automated backups complete daily with 1-hour recovery time objective
- NFR16: Failed categorization batches automatically retry up to 3 times
- NFR17: System maintains audit trail integrity with no data loss