Technical Companion
Architecture for material intelligence.
Subsystems are described by purpose, inputs, outputs, current status, and future work.
Diagrams
Original SVG diagrams.
Subsystems
Purpose, inputs, outputs, and future work.
Frontend
- Purpose
- User-facing portal for intake, dashboards, reports, research, and public communication.
- Inputs
- User forms, image uploads, asset metadata, organization context, workflow selections.
- Outputs
- Asset records, intake confirmations, dashboards, evidence views, downloadable reports.
- Current Status
- Public website live. Research portal generated in v2.0. Product UI remains prototype-stage.
- Future Work
- Role-based dashboard, upload experience, report preview, accessibility hardening, offline collection mode.
Backend
- Purpose
- Application services for asset records, workflow state, authorization, audit logging, and integrations.
- Inputs
- Frontend requests, API calls, processor updates, model outputs, file metadata.
- Outputs
- Validated records, workflow events, audit logs, signed report artifacts, integration responses.
- Current Status
- Architecture defined. No production backend deployment claimed.
- Future Work
- Event-driven services, tenant model, RBAC, immutable audit trail, integration adapters.
AI Layer
- Purpose
- Decision-support layer for classification, condition analysis, forecasting, recommendation, and text assistance.
- Inputs
- Images, asset metadata, historical records, price signals, route data, policy references.
- Outputs
- Predictions, confidence scores, recommended pathway, explanation notes, exception flags.
- Current Status
- Concept and prototype direction. No production model metrics claimed.
- Future Work
- Dataset design, baseline models, human review loop, evaluation harness, model cards.
Computer Vision
- Purpose
- Analyze device photos and component images for visible condition and category signals.
- Inputs
- Device images, component photos, labels, damage views, battery and PCB images.
- Outputs
- Category, condition flags, visible component hints, safety warnings, confidence score.
- Current Status
- Research and prototype design.
- Future Work
- Image capture protocol, labeled dataset, active learning, false-negative review for safety-critical classes.
Forecasting
- Purpose
- Forecast collection volume, route demand, downstream capacity, and market movement.
- Inputs
- Time series intake, geography, partner capacity, seasonality, price history.
- Outputs
- Demand forecasts, capacity warnings, pickup windows, planning signals.
- Current Status
- Proposed future capability.
- Future Work
- Synthetic planning model, pilot data capture, calibration against real operations.
Pricing Engine
- Purpose
- Compare economic pathways for resale, repair, parts harvesting, recycling, or destruction.
- Inputs
- Asset category, condition, component signals, market data, processing costs, logistics costs.
- Outputs
- Pathway economics, value range, uncertainty, recommended handling route.
- Current Status
- Architecture concept only.
- Future Work
- Market source governance, price confidence bands, override logs, regional constraints.
Carbon Intelligence
- Purpose
- Estimate emissions implications of alternative recovery pathways with visible assumptions.
- Inputs
- Asset weight, material class, route, recovery pathway, emissions factors, evidence quality.
- Outputs
- Estimated carbon impact, factor provenance, uncertainty band, methodology note.
- Current Status
- Research-stage methodology.
- Future Work
- Factor registry, methodology review, uncertainty scoring, audit export.
ESG Engine
- Purpose
- Prepare evidence packets for Scope 3, diversion, chain-of-custody, and recovery reporting.
- Inputs
- Asset events, custody updates, processor records, carbon estimates, report templates.
- Outputs
- Evidence packet, status flags, report export, data gaps, confidence labels.
- Current Status
- Report architecture defined. No audited reports claimed.
- Future Work
- Report templates aligned to customer needs, assurance workflow, API export.
API Layer
- Purpose
- Allow organizations and processors to exchange asset, workflow, and report data.
- Inputs
- External system requests, webhooks, batch imports, processor confirmations.
- Outputs
- Asset status, event updates, report data, integration notifications.
- Current Status
- Future capability.
- Future Work
- REST/GraphQL schema, webhooks, OAuth, rate limits, audit logging.
Cloud Infrastructure
- Purpose
- Secure, scalable runtime for data storage, models, file handling, and observability.
- Inputs
- Application traffic, model jobs, uploaded files, logs, metrics.
- Outputs
- Available services, monitored jobs, secure storage, backups, deployment telemetry.
- Current Status
- Not deployed as production infrastructure.
- Future Work
- IaC, secrets management, encrypted storage, monitoring, cost controls.
Data Pipeline
- Purpose
- Move asset evidence from intake through model processing, decisioning, reporting, and analytics.
- Inputs
- Raw uploads, validation events, model outputs, human decisions, downstream confirmations.
- Outputs
- Curated records, feature tables, event log, analytics views, exportable evidence.
- Current Status
- Architecture complete at concept level.
- Future Work
- Schema registry, validation rules, event bus, lineage tracking, data retention policy.