W WarehouseAI
How it works

Four steps. Designed so you can quit after step two.

Most consulting engagements lock you in before they prove anything. Ours doesn't. The first four weeks are scoped to produce a working pilot you can keep or kill. If the numbers don't beat your current process, you walk with the audit and the data work, no further commitment.

1

Audit

One to two weeks. We read your WMS data model, sit with your operations lead, walk the floor, and watch a shift. The output is a written audit: what your data can support today, what's missing, where the highest-ROI AI bet is, and what we'd need to access to ship a pilot. No black-box scoring. We tell you what we'd build first and why, in plain English.

If the audit shows your data isn't ready — happens maybe one in five times — we say so and stop. You keep the audit.

2

Pilot

Four weeks. We pick one slice — usually a single SKU category, a single client account, or a single shift — and ship one module against it. Inventory forecasting on your top 200 SKUs. Pick optimization on your busiest aisle. Auto reporting for one demanding client. Whatever the audit said had the highest ratio of pain to engineering effort.

At the end of four weeks we put the numbers next to your baseline. Walk time, fill rate, hours saved, dollars recovered. Your call whether to continue.

3

Roll out

Four to eight weeks depending on scope. We extend the pilot from one slice to the full floor, or layer in a second and third module from the audit. Integration with your WMS, dashboards your operations lead actually opens, alerts that route to the right person not the inbox graveyard.

By the end of rollout you have working AI in the operational loop, not a Powerpoint deck. Picker handhelds reroute. Client reports send themselves. The night-shift lead gets a Slack ping when something drifts.

4

Tune

Ongoing, monthly. Models drift when reality changes. Demand patterns shift. New SKUs come in, old ones get retired, a major client triples their volume. Once a month we sit down with your team, look at how the models are actually performing against the floor, and tune. Re-train on the last 90 days, adjust thresholds, retire alerts that became noise, add new ones for problems that emerged.

This is the part most AI projects skip. It's also the part that determines whether the work you paid for in months one through three is still creating value in month nine.

Step one is a conversation. Start there.

Tell us your WMS, your rough volume, and the two or three things that frustrate your floor lead the most. We'll reply within 24 hours with whether the audit is worth booking and what it would look like.