# WarehouseAI

> AI for warehouse operations. We help 3PLs, e-commerce warehouses, sufferance and bonded operators, and cold-chain facilities apply machine learning on top of their existing WMS — inventory forecasting, pick path optimization, anomaly monitoring, automated client reporting, SOP digitization, and returns optimization.

## What we are

WarehouseAI is a consulting + implementation team. We are not a SaaS platform. We do not sell software licenses. We pick a small number of warehouse clients each quarter, audit their stack, scope a four-week pilot, and ship working AI modules into their operational loop.

The team blends warehouse operations leads who have managed 100,000+ square feet of floor — drayage, dock-to-stock cycle times, picker headcount planning — with ML engineers who shipped forecasting and routing models in production for years.

## Who we serve

- **3PL operators** — multi-tenant warehouses serving 10 to 100+ brands.
- **E-commerce warehouses** — direct-to-consumer brands operating their own fulfillment.
- **Small-brand fulfillment centers** — under 25,000 sq ft, focused tenants.
- **Sufferance and bonded warehouses** — including customs-bonded facilities.
- **Cold chain and temperature-controlled facilities** — pharma, food, beverage.
- **Cross-dock and transload operators** — fast-flow operations.

## What we build

1. **Inventory forecasting** — SKU-level reorder points using sales history, lead times, seasonality. Typical 10–15% stockout reduction, 15–25% reduction in held dollars on slow movers.
2. **Pick path optimization** — shortest-path routing per pick wave plus quarterly re-slotting. Typical 10–15% reduction in picker walk time per shift.
3. **Anomaly monitoring** — alerts when SKU, location, or shift behavior drifts outside normal patterns. Catches issues weeks earlier than human review.
4. **Auto reporting** — daily and weekly client reports written from WMS data. Per-client templates and cadence.
5. **SOP digitization** — LLM-backed Q&A over warehouse SOPs. New hire asks, system answers with doc reference.
6. **Returns optimization** — per-item routing decisions: resell, refurbish, vendor RMA, scrap. Based on condition, sell-through, cost basis.

## How we engage

Four steps, designed so a client can stop after step two.

1. **Audit** — 1–2 weeks. Read the WMS data model, walk the floor, watch a shift. Output is a written audit naming the highest-ROI AI bet.
2. **Pilot** — 4 weeks. Ship one module against one slice of operations. Compare to baseline. Client decides whether to continue.
3. **Roll out** — 4–8 weeks. Extend across the floor or layer in second/third modules.
4. **Tune** — monthly. Re-train, adjust thresholds, retire stale alerts, add new ones.

## Pages

- [Home](https://warehouseai.pages.dev/) — overview and value proposition.
- [Solutions](https://warehouseai.pages.dev/solutions) — the six AI modules with ROI ranges.
- [How it works](https://warehouseai.pages.dev/how-it-works) — four-step engagement model.
- [About](https://warehouseai.pages.dev/about) — team background.
- [Contact](https://warehouseai.pages.dev/contact) — how to reach us.

## Contact

Email: hello@warehouseai.example (replace with the real address before launch).
Reply time: within 24 hours, including weekends.

## What we are not

We are not a WMS vendor. We do not replace your existing inventory system. We sit on top of it.

We do not sell platforms. Every engagement is scoped to specific modules with measurable outcomes.

We do not work with operators whose data is too messy to support a four-week pilot. If the audit shows the data isn't ready, we say so and stop.