Eckeka (2025–Present) — Packaging Risk & EPR Intelligence MVP

Technical Stack

Problem

Packaged goods companies in Kenya face regulatory and reputational pressure around packaging sustainability and EPR compliance. NEMA requires reporting and fee management. Packaging data is fragmented across spreadsheets and suppliers. Brands lack quantified recyclability and circularity risk scoring. Compliance tracking is manual and error-prone.

Solution

A packaging risk and transparency intelligence platform that allows brands to:

1. 01Inventory SKUs with detailed material and design data
2. 02Automatically compute recyclability and circularity scores
3. 03Track EPR compliance (NEMA registration, reporting, fee status, PRO partnerships, material targets)
4. 04Visualize packaging risk and material distribution
5. 05Generate AI-powered reports (board summaries, risk assessments, EMF format, transparency snapshot, 30/60/90 day roadmap, CSV exports)
6. 06Provide cross-company admin visibility

The platform is framed around risk exposure and transparency, not compliance guarantees.

Architecture Overview

A Next.js application with Supabase backend. The platform includes authentication, a dashboard for inventory and analytics, SKU management with material data, automated scoring, compliance tracking, AI-generated reports, and admin tools. Row Level Security provides per-user data isolation at the database level.

Automated Scoring Engine

Recyclability and circularity scoring is computed automatically at the database level whenever SKU data changes.

• Base score by material type
• Packaging modifier
• Design penalties (shrink sleeves, dark coloring, incompatible caps)
• Circularity bonuses (mono-material, reuse potential, renewable %, certifications)
• Normalized output score (0–100) with risk level classification

AI Report Generation

Generates structured reports using GPT-4 with streaming output, rendered as HTML with PDF export and clipboard copy support.

• Full Risk Assessment
• Board Summary
• EMF Global Commitment format
• Transparency Snapshot
• Action Roadmap (30/60/90)
• SKU Data Export (CSV)

Key Technical Decisions

• Server-side data fetching for dashboard pages
• Database-level computation instead of client-side scoring for consistency
• Row Level Security across all core tables for per-user data isolation
• Transactional email via Resend

Tradeoffs

• Database-level scoring adds migration complexity but eliminates client-server drift and ensures consistency
• Row Level Security adds policy management overhead but provides per-user data isolation without application-layer checks
• Streaming AI reports increase server costs but enable real-time rendering of long-form documents

Outcome

Lessons

• Database-level scoring eliminates an entire class of consistency bugs between frontend and backend
• Row Level Security is a strong default for multi-tenant SaaS — it pushes authorization into the database layer
• Streaming AI responses provide a clean pattern for long-form generated content