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Agent 07 · Retail

InventoryIQ Agent

Keeps stock data clean and drafts purchase orders before items run out.

Agent PackDineDeskLangGraph

Found42
Fixed + verified42
Held for approval0
Recall1.00

One offline run on demo data with planted problems (recorded platform test, November 2026).

How InventoryIQ heals data, step by step
Client problem
Stock-outs lose sales; bad inventory counts make reorder rules useless.
What it does
Fixes negative/impossible stock and missing sales, then predicts stock-out risk per SKU-store.
How it heals
Sets negative stock to zero with a recount task, imputes missing sales from the store-product median, drafts purchase orders for stock-out risks and flags overstock for markdown.
Human gate
POs above a value limit wait for the buyer.
Typical buyer
Multi-store retailers, restaurants, distributors
Built with
LangGraph self-healing loop
Demo data
Kaggle anirudhchauhan/retail-store-inventory-forecasting-dataset (or synthetic equivalent).
Tables
inventory ← Kaggle anirudhchauhan/retail-store-inventory-forecasting-dataset
Target outcome
Target: 20-30% fewer stock-outs on fast movers.
Path to production
DineDesk / Commerce Pack with POS export or API.
Issues detected on the demo data
IssueRiskProposed fixCount
out_of_range_inventory_level
inventory_level=-55 outside [0, None]
lowSet to 0 and create a physical recount task6
missing_units_sold
'units_sold' is empty
lowImpute from the store-product median6
duplicate_record
Duplicate on record_id
lowDrop duplicate stock record5
stockout_risk
P0001 at S001: 2.0 days of cover
mediumDraft purchase order for 7 days of demand25

How we demo it

  1. Load retail inventory with planted errors.
  2. Run InventoryIQ: data repaired, PO drafts created.
  3. Open the PO drafts file.

Built on AWS

Runs in the platform container on EC2 Graviton (or on demand in Lambda), writes healed tables to the S3 lakehouse, uses Amazon Bedrock only to write explanations, and reports six metrics to CloudWatch.

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