60% reduction in stockout incidents and 15+ hours/week of founder time reclaimed on inventory ops.
You built a DTC brand past $100K/month in revenue, and now you are drowning in inventory. Every morning starts with a manual stock check across multiple warehouses and 3PL partners. You are toggling between Shopify, ShipStation, supplier portals, and spreadsheets trying to answer a simple question: are we going to run out of anything this week?
The answer is usually "probably, but I do not know which SKU until it is too late." Static reorder points break the moment demand shifts, a TikTok mention, a flash sale, a competitor stockout, and by the time your spreadsheet catches up, your bestseller has been out of stock for three days. Every stockout is not just a lost sale: 30% of customers buy from a competitor, and 9% never come back.
Meanwhile, the opposite problem compounds silently. Dead stock accumulates because nobody is tracking which SKUs stopped moving. COGS calculations are rough estimates because landed costs (unit price + freight + duties + handling) vary by shipment and nobody has time to reconcile them. Warehouse discrepancies between physical count and system counts go unnoticed until a big order hits.
Maya is your AI Operations & Inventory Manager. She pulls stock data from every warehouse and 3PL partner at 6 AM, tallies available inventory per SKU, calculates velocity and 30-day sales trends, and flags anything trending toward a stockout. At 8 AM, you get a single Slack message: "SKU-2847 has 12 units left, 8.3/day velocity, 1.5 days to stockout. Reorder 500 from Zhang at $4.20/unit? [Approve PO] [Adjust Qty] [Get New Quote]." You tap Approve on your phone. That is your entire involvement.
That is why you need Maya.
Each step is automated. Maya only escalates when human judgment is required.
Maya consolidates stock levels across all locations (Shopify, ShipStation, 3PL warehouse systems), tallies total available per SKU, and calculates sell-through velocity based on 14-day and 30-day sales windows.
Maya generates a prioritized reorder alert with current stock, velocity, estimated days to stockout, recommended quantity, supplier pricing, lead time, and landed cost estimate, sent to the founder via Slack with [Approve] [Adjust] [Get Quote] buttons.
Maya cross-checks yesterday’s shipped orders against carrier scan data, flags orders marked shipped but not picked up by carrier, and alerts the ops team to warehouse discrepancies.
Maya identifies SKUs with fewer than 3 units sold in 60 days, calculates carrying cost and tied-up capital, and drafts liquidation or markdown recommendations with projected margin impact.
Maya recalculates stockout timelines for affected SKUs, checks if existing POs will arrive in time, and sends an urgent alert if the spike will cause a stockout before the next delivery, with expedited shipping cost comparison.
Maya sends a daily ops summary to the founder: total units shipped, stockout risk list, COGS update by batch, warehouse utilization rate, and any discrepancies flagged. The owner reads it in 90 seconds.
Clear boundaries. Maya works autonomously within defined limits and escalates everything else.
Maya connects to the platforms you already use. No new software to learn.
Maya is deployed gradually with measurable checkpoints at every stage.
Monitoring mode first, then gradual rollout.
Pilot begins with the top 50 SKUs by revenue, the products where stockouts cause the most financial damage. Week 1-2 Maya runs in monitoring mode, pulling data, calculating velocity, and generating alerts that the ops team evaluates without changing sourcing processes.
Full validation before production deployment.
These AI employees share data and coordinate with Maya to cover your full operation.