Nauta brings AI-native inventory intelligence to the heart of importer operations
Inventory gaps don't just hurt sales. They trigger chargebacks, lost contracts, and chaos across teams. Nauta's new Inventory Optimization Engine was built to cut that off at the root by predicting stockout risk and giving operators a clear plan of action, not a report after the damage is done.
The pitch is simple: unify the data importers already have, learn how the business actually runs, and surface the few decisions that protect revenue. Less firefighting. More control.
Key takeaways
- Proactive stockout prevention: Predictive signals at the SKU level help teams spot risk early and prioritize what to move, expedite, or reallocate.
- Unified data layer: Integrates ERP, WMS, and TMS data-plus emails and documents-so procurement, transportation, merchandising, and operations work from one source of truth.
- Agentic AI that learns your rules: The system absorbs business logic (safety stock, approvals, exception paths), suggests actions, and executes with human approval.
- Material impact on revenue: Even a 0.5% lift in fulfillment can translate into millions saved across retailers facing an ~$80B annual drag from inventory mismanagement in the U.S.
Why this matters for operations
Disconnected systems, inconsistent product codes, and siloed teams force operators to make high-stakes calls with incomplete data. That's fine-until peak hits or demand spikes. Then the wheels come off.
Nauta's engine centralizes data and turns it into forward-looking decisions. It shows where exposure lives, what levers are available, and the likely impact of each move. That's the difference between rushing an airfreight booking and planning a clean replenishment path weeks earlier.
How it works
- Data harmonization: Pulls from ERP, WMS, TMS, order portals, and even email/documents to standardize SKUs, locations, and event timelines.
- Predictive analytics: Forecasts stockout probability, days of supply, and fill-rate risk by SKU, channel, and node.
- Agentic workflow: Learns how your teams approve replenishments, define safety stock, and handle exceptions. Proposes actions aligned to those rules; executes once approved. Autonomy increases as confidence grows.
A note from leadership
"Shippers need clarity on where their inventory is during the holiday season. They can only serve their customers if they know how it's moving, and what's likely to run short," said Valentina Jordan, CEO and Co-Founder of Nauta. "We're giving shippers a clear understanding of their exposure down to the SKU level. Customers can see where stockout risk exists and take action before issues happen, saving them hundreds of thousands in lost revenue and penalties."
What changes day to day
- Morning standups: Start with a ranked list of at-risk SKUs, root causes, and recommended actions (reallocate, expedite, pull ahead POs, adjust safety stock).
- Cross-team alignment: Procurement, transportation, and merchandising see the same data and the same impacts, which cuts decision time and back-and-forth.
- Exception management: Ops focuses on the 5% of items that drive 80% of risk instead of combing through reports.
Quick scenario
A top seller is projected to hit a three-day stockout in the West DC next week. The engine flags it, shows on-water inventory two weeks out, and recommends reallocating current inbound from the Gulf to the West plus a targeted DC-to-DC transfer. Transportation gets an optimal plan; merch approves channel limits; finance sees the avoided penalties. Problem solved before it hits the shelf.
Implementation checklist for operators
- Connect ERP, WMS, TMS, and order data; standardize SKU and location IDs.
- Define safety stock logic, approval thresholds, and exception paths by role.
- Set guardrails for automation (what the AI can propose vs. auto-execute).
- Pilot with a high-velocity category and two DCs; expand once signal quality is proven.
- Instrument KPIs and feedback loops so the system keeps learning.
Metrics to watch
- Fill rate and on-time, in-full (OTIF) by channel
- Stockout probability and days of supply by SKU/location
- Expedite costs vs. planned freight
- Chargebacks/penalties and lost sales avoided
- Inventory turns and working capital impact
Why the $80B problem is worth solving first
Inventory is the linchpin. Stockouts ripple through distributors, retailers, and manufacturers. Small percentage shifts in fulfillment compound into real dollars, especially in thin-margin categories.
Nauta focused early on the data foundation required to fix this-because without clean, unified data, no model can reliably call risk or recommend moves you'd trust.
What's next: payments
Backed by $7M in seed funding and growing revenue, Nauta is exploring payments-the last link many importers still manage with manual checks and fragmented portals. If goods, data, and money sit on one platform, operators gain visibility and leverage across all three.
Practical next steps
- Audit where stockouts occur most often: item, node, channel, or vendor.
- Quantify the cost: lost sales, penalties, expedites, DC labor overruns.
- Map decisions you'd automate with guardrails (e.g., reallocate up to X%, greenlight expedites under $Y, auto-notify vendors when safety stock breaches).
- Stand up a four-week pilot and commit to a weekly review cadence.
If you're upskilling your ops team to work with AI-driven workflows and data, here's a curated path worth bookmarking: AI courses by job role.
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