Automation
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A custom workflow architecture that automatically identifies near-expiry inventory, matches it with local food bank capacity, and orchestrates pickup and documentation to reduce waste and maximize tax deductions.
This workflow automates the manual, time-intensive coordination required to divert edible, near-expiry goods from landfills to food banks. It directly attacks waste cost, generating measurable ROI through reduced disposal fees, optimized tax deductions, and improved ESG metrics. The system integrates with your WMS or ERP (e.g., SAP, Oracle) to monitor SKU-level shelf-life data, using predefined business rules to flag inventory for donation eligibility based on remaining days and product type.
Implementation requires building an orchestration layer, likely with LangGraph or a similar framework, to manage the multi-step logic and external API calls. Critical controls include a human-in-the-loop approval gate for high-value items, exception routing for mismatches, and immutable logging for audit and tax purposes. The architecture must integrate with label printers, transportation management systems, and financial software to create a closed-loop, compliant process that scales across distribution centers.
A custom workflow that automates the identification, matching, and documentation of near-expiry goods for donation, turning a manual, loss-prone process into a system for recovered value and community impact.
Automated valuation and documentation of donated goods ensures accurate, IRS-compliant records for every pallet. This transforms a pure disposal cost (landfill fees) into a recoverable tax asset, directly improving P&L. The workflow integrates with ERP (e.g., SAP, Oracle) to capture original cost and automate the generation of Form 8283 attachments.
Replaces hours spent on phone calls, spreadsheets, and email chains checking food bank capacity, pickup schedules, and product suitability. Orchestration agents (built with LangGraph or CrewAI) match inventory attributes with recipient requirements and logistics constraints, freeing staff for higher-value tasks.
Proactive routing prevents edible food from ever reaching the compactor. By dynamically connecting near-expiry inventory to the donation pipeline, the system minimizes waste volume, directly cutting disposal costs and reducing Scope 3 emissions linked to landfill decomposition, supporting corporate sustainability targets.
Agents continuously monitor WMS for SKUs approaching expiry, flagging them for priority donation routing. This clears valuable warehouse space faster than waiting for the last possible sale date, improving slot utilization for fresh product and reducing the capital tied up in aging, at-risk inventory.
Automated, reliable donations build stronger, data-driven partnerships with local food banks. The system provides transparency into volume and type of goods donated, enabling impactful storytelling and CSR reporting. This mitigates reputational risk associated with food waste while creating measurable social goodwill.
In regions with organic waste bans or producer responsibility laws, automated compliance is critical. The workflow logs every diversion attempt and success, creating a defensible audit trail that demonstrates proactive waste reduction efforts, protecting against potential fines for non-compliance.
A custom multi-agent workflow that automatically identifies near-expiry inventory, matches it with local food bank capacity, and orchestrates donation logistics to reduce waste and maximize tax benefits.
This workflow automates the manual, time-intensive process of coordinating food donations, directly attacking the operational bottleneck of waste management. It converts potential landfill loss into community benefit and tax deductions by systematically routing edible, near-expiry goods. Savings come from reduced disposal fees, optimized labor, and recovered asset value through documented charitable deductions, while strengthening ESG metrics and regulatory compliance posture.
Implementation integrates with SAP, Oracle, or custom WMS via APIs to ingest real-time inventory data. Specialized agents, built on frameworks like LangGraph, handle matching logic against food bank APIs (e.g., Feeding America network), generate GS1-compliant documentation, and update financial systems for deductions. Critical controls include human-in-the-loop approval for high-value lots, exception routing for logistical constraints, and full audit trails for compliance and financial reporting.
A custom agentic architecture that automates the identification, matching, and documentation of near-expiry inventory for donation, turning a manual coordination burden into a scalable, tax-advantaged waste-reduction system.
This agent continuously queries the Warehouse Management System (WMS) and Enterprise Resource Planning (ERP) system (e.g., SAP, Oracle) for SKUs approaching their 'best before' or 'sell by' date. It ingests real-time data from IoT temperature loggers to adjust shelf-life forecasts dynamically, identifying which pallets are viable for donation versus those that must be routed to waste processing. It creates a prioritized queue of candidate inventory.
A specialized agent integrates with food bank APIs (like Feeding America's network) and local charity schedules. It matches inventory type, volume, and location against recipient capacity, refrigeration requirements, and upcoming pickup routes. It uses geospatial logic to minimize transportation cost and time, ensuring the donation is logistically feasible before proceeding.
This component automates the generation of IRS Form 8283 and donation receipts. It pulls product cost data from the ERP, applies fair market value calculations per IRS guidelines, and assembles a complete documentation package. It routes the package through an internal approval gate (e.g., in ServiceNow) for finance sign-off before release, creating a defensible audit trail for deductions.
Upon final approval, this agent triggers physical execution. It generates a pick ticket in the WMS, directing forklift operators or Autonomous Guided Vehicles (AGVs) to the specific pallet location. It updates the inventory status to 'Donation Hold,' prints shipping labels, and sends digital instructions to the loading dock, ensuring the goods are staged correctly for the charity's carrier.
A critical control layer that manages edge cases. If the matching agent finds no suitable recipient, it routes the inventory to a human review queue for manual broker matching or waste decision. It also handles discrepancies (e.g., damaged goods found during picking) by pausing the workflow, alerting quality teams, and re-initiating the matching process for the remaining quantity.
The observability layer built on tools like Grafana or Datadog. It tracks key metrics: pounds diverted from landfill, estimated tax deduction value, processing time per donation, and charity fulfillment rates. This dashboard provides operational proof for sustainability reporting and identifies bottlenecks for continuous workflow optimization.
A phased implementation blueprint for a custom agentic workflow that automates the identification, matching, and documentation of near-expiry inventory for donation, turning waste into tax deductions and community benefit.
Phase 1 establishes the core orchestration logic, connecting your Warehouse Management System (WMS) or ERP to a donation-matching engine. An agent continuously queries inventory for SKUs within a configurable expiry window (e.g., 7 days). For each candidate, it retrieves real-time data on local food bank capacity, pickup schedules, and refrigeration requirements from an integrated partner API. This initial build automates the manual search and phone-call bottleneck, creating a match-ready pipeline and providing the first layer of operational visibility and control.
Phase 2 integrates documentation and compliance. Upon a successful match, the workflow automatically generates donation paperwork, including product manifests and tax-deductible receipts, by pulling data from the WMS and your financial system. Phase 3 adds observability and closed-loop control, implementing dashboards to track diverted pounds, tax value recovered, and exception rates. The final architecture delivers measurable ROI through reduced landfill fees, optimized tax benefits, and the elimination of manual coordination labor, while ensuring audit-ready governance over the entire donation lifecycle.
Manual vs. automated workflow comparison for routing near-expiry inventory to food banks, measuring impact on waste reduction, tax recovery, and operational cost.
| Metric | Manual Coordination Process | Custom Agentic Workflow |
|---|---|---|
Cycle Time from Identification to Pickup | 5-7 business days | < 4 hours |
Percentage of Eligible Goods Diverted from Landfill | ~35% |
|
Labor Hours per Donation Event | 8-12 hours | 1.5 hours (exception review only) |
Tax Documentation Accuracy & Completeness | 70%, requires post-audit | 98%, auto-generated & validated |
Average Cost per Pound Diverted (Labor, Logistics, Admin) | $0.85 | $0.22 |
Food Bank Partner Satisfaction (On-time, Accurate Pallet) | Low, frequent mismatches | High, automated capacity matching |
Audit Trail for Compliance & Sustainability Reporting | Fragmented spreadsheets & emails | Unified, immutable ledger with GS1 lot links |
Practical questions about implementing a custom agentic workflow to automatically match near-expiry inventory with food bank capacity, generate donation documentation, and maximize tax deductions.
A robust implementation uses a multi-agent validation layer. A primary agent ingests data from WMS, ERP, and direct barcode scans via mobile devices. A secondary verification agent cross-references this against production records and purchase orders. For ambiguous dates (e.g., 'BEST BY' vs 'USE BY'), a rules engine applies business logic based on product category. All low-confidence items are routed to a human-in-the-loop exception queue for manual verification via a tablet app before any donation action is taken, ensuring the system's decisions are grounded in accurate data.
A production-grade donation routing system requires robust governance, clear approval gates, and a phased rollout to manage operational risk while maximizing tax benefits and community impact.
Initial governance is established by codifying business rules into the orchestration layer—typically built with LangGraph or Temporal—which enforces donation eligibility thresholds, prioritizes recipient partners based on capacity and pickup schedules, and integrates with ERP systems like SAP or Oracle for inventory visibility. A control panel allows compliance and operations teams to set parameters for tax deduction calculations, manage recipient certifications, and define escalation paths for exceptions, ensuring the automated workflow aligns with both financial and social responsibility policies before any goods are moved.
Phased rollout begins with a single distribution center and a limited set of non-perishable SKUs, instrumented with full observability into match accuracy, exception rates, and document generation. This pilot phase validates data quality from warehouse management systems and recipient APIs. Subsequent phases expand to include refrigerated goods, more complex tax scenarios, and integration with transportation management systems for automated pickup scheduling. Each phase is gated by a review of operational metrics and control effectiveness, ensuring the system scales without introducing undue financial or reputational risk.
A production-grade donation routing workflow connects real-time inventory, logistics, and compliance systems to automate the matching and movement of near-expiry goods to food banks, turning a manual coordination burden into a measurable waste-reduction and tax-advantage engine.
The workflow ingests real-time SKU-level data—including lot codes, expiration dates, and warehouse location—from systems like SAP EWM or Oracle WMS. An agent continuously scans for items within a configurable expiry window (e.g., 7-14 days), filtering out items prohibited for donation by quality holds or regulatory status. This replaces manual spreadsheet audits and creates the primary trigger for the routing logic.
A dedicated integration agent polls partner food banks via custom APIs or platforms like Food Rescue Hero to pull real-time data on: storage capacity (refrigerated vs. dry), pickup schedules, product preferences, and geofenced service areas. This data is vectorized and matched against available inventory profiles to generate feasible donation options, eliminating hours of phone and email coordination.
Upon matching, a documentation agent automatically generates IRS-compliant donation forms (e.g., Form 8283 drafts) by pulling SKU data, Fair Market Value from internal tables, and food bank EIN data. It validates against internal gift-in-kind policies and routes the packet for digital signature via DocuSign or Adobe Sign integration. This systematizes a high-value but often neglected step, maximizing deductible value and audit readiness.
For donor-coordinated pickup, an orchestration agent creates a shipment order in the TMS (e.g., Blue Yonder, Manhattan Associates) with optimized load-building instructions. It communicates pickup windows and BOL details back to the food bank and warehouse systems. For food-bank-coordinated pickup, it generates appointment slots and passes labels/ASNs. This closes the loop between donation decision and physical execution.
The workflow enriches decisions with historical data from the quality management system (QMS) and traceability platform. An agent checks the temperature history of cold-chain items from IoT logs and reviews the lot genealogy for any linked quality incidents or supplier issues. This prevents the routing of potentially compromised goods, protecting brand reputation and recipient safety.
All proposed donations are logged in a central dashboard (built in tools like Retool or connected to Power BI) that serves as the human-in-the-loop control plane. It displays match rationale, estimated tax value, and logistics summary for manager approval. Post-execution, it tracks key metrics: pounds diverted, carbon equivalent saved, and processing cost per donation. This provides the governance and ROI measurement layer essential for scaling the program.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
This page details a custom automation architecture that identifies near-expiry inventory, matches it with recipient capacity, and orchestrates donation logistics to convert waste into tax deductions and community benefit.
A dynamic donation workflow automates the manual, time-sensitive coordination between warehouse teams, food banks, and logistics providers. It ingests real-time inventory data from a WMS or ERP like SAP, applies shelf-life models to flag SKUs approaching expiry, and evaluates local food bank capacity, refrigeration needs, and pickup schedules. The operational upside comes from maximizing tax deductions, reducing landfill fees, and fulfilling corporate social responsibility goals without burdening staff with phone calls and spreadsheet tracking. The architecture must handle variable data quality from legacy systems and include approval gates for high-value donations.
Implementation requires integrating the orchestration layer—built with frameworks like LangGraph or Temporal—with core enterprise systems. The matching engine queries external food bank APIs or a maintained recipient database, while document agents generate packing slips and tax forms. Critical controls include human approval for high-value lots, exception routing for data mismatches, and continuous learning from donation outcomes to refine shelf-life predictions and recipient preferences. Observability dashboards track metrics like pounds diverted, tax value recovered, and fulfillment cycle time to prove ROI.
This table compares the operational and financial tradeoffs between manual coordination, basic automated rules, and a custom agentic workflow for routing near-expiry inventory to food banks.
| Metric | Manual Process (Current State) | Rules-Based Automation (Partial Solution) | Custom Agentic Workflow (Target Architecture) |
|---|---|---|---|
Cycle Time from Identification to Pickup | 3–5 days | 24–48 hours | 45–90 minutes |
Human Coordination & Review Rate | 100% of matches and schedules | ~40% (exceptions & complex cases) | < 20% (high-confidence exceptions only) |
Tax Deduction Capture Rate | ~65% (incomplete documentation) | ~80% (structured but inflexible) |
|
Food Recovered vs. Landfilled (Monthly) | ~40% of eligible inventory | ~60% of eligible inventory |
|
Operational Cost per Donation Pallet | $45–$60 (staff time, calls, admin) | $20–$30 (system maintenance, exception handling) | $5–$10 (cloud compute, monitoring, minor oversight) |
Audit Trail & Documentation Completeness | Fragmented (emails, spreadsheets, calls) | Structured but siloed (system logs, basic reports) | End-to-end, immutable (timestamped agent decisions, matched records, digital CoA) |
Ability to Handle Multi-Variable Constraints | None (reactive, best-effort) | Limited (pre-defined rules for capacity/distance) | High (real-time optimization for capacity, schedules, vehicle type, product compatibility) |
System Integration & Data Flow | Manual entry across WMS, ERP, spreadsheets | Point-to-point APIs for inventory status only | Orchestrated agents connecting WMS, ERP, TMS, food bank portals, and document systems |
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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