DPL-008Agentic AIdeployed · live
Signal — Demand Forecasting
Forecasting agents that predict demand per SKU and rebalance stock automatically.
- client
- Larkfield Retail
- sector
- Retail
- timeline
- 12 weeks
- shipped
- 2025
31%
fewer stockouts
-22%
overstock
18%
margin lift
case_file
- stage_01 · diagnose
The challenge
Buyers forecast by gut, so shelves swung between stockouts and dead inventory. Every markdown and missed sale traced back to a guess.
- stage_02 · build
The approach
We built forecasting agents that predict demand per SKU per store, then propose reorders and rebalances. Buyers approve, the system executes, and it learns from every cycle.
- stage_03 · ship
The outcome
Stockouts fell 31% and overstock shrank at the same time, with buyers steering strategy instead of wrestling spreadsheets.
31%fewer stockouts
scope_of_work
What we owned on this engagement, end to end.
- 01Forecasting models
- 02Agentic automation
- 03Inventory integration
- 04Evals
build_manifest
The runtime this system ships and runs on in production.
dependencies06 loaded
PythonTypeScriptOpenAIClickHousePostgresAWS
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