Inventory forecasting LLMs.

Why now

Inventory management is the second-biggest pain after marketing. AI-driven forecasting beats Excel sheets.

What the signal looks like

Repos with Shopify / Amazon / WooCommerce adapters, time-series ML libraries, and procurement integration flows.

Public examples

We name publicprojects + categories only, never founders we track inside the paid product. The buyer’s edge stays inside the product.

• Inventory Planner shape
• Cogsy
• Open-source demand-forecasting libraries

What this displaces

An Excel sheet maintained by the founder.

How to validate it in an afternoon

Before committing build time or a thesis memo to inventory forecasting llms, run three cheap checks against public engineering activity. Each takes minutes and none require access to private data.

1. Count active builders. Search GitHub for repositories matching this category, then check how many accepted commits in the last 14 days. More than a handful of active teams means the category has energy, not just mentions.
2. Look for the steady, one deal per month pattern in funding. If funded companies keep appearing here, a round closes somewhere in this category most quarters, neither hot nor dead. Cross-check the e-commerce infrastructure leaderboard to see whether any of the accelerators sit adjacent to this niche.
3. Test the one-quarter build cost assumption honestly: expect a quarter of sustained build time, usually two or three people, before first external users. If your calendar cannot absorb that, the opportunity is real but not yours yet.

The weekly signal feed tracks 10 E-commerce Infrastructure sub-niches including this one, so the cohort side of this check can run continuously instead of manually.

Common questions about this niche

Buyer?

DTC + multi-SKU e-commerce brands.

Pricing?

$200-2k/mo based on SKU count.

Moat?

Accuracy + platform integrations.