AI Inventory Optimization Software for CPG, Retail, and Consumer Brands
TrueGradient combines multi-echelon inventory optimization (MEIO), probabilistic safety stock, and agentic AI exception monitoring to release working capital without compromising service levels.
Inventory is the largest controllable line on the supply chain P&L — and the hardest to size correctly. Too much inventory traps cash and feeds markdowns. Too little kills service levels and account relationships. TrueGradient's AI inventory optimization software engineers stock targets to your service-level commitments using the full demand uncertainty distribution, then continuously monitors exceptions so planners only intervene where it matters.
Trusted by leading CPG, retail and consumer brands
Multi-echelon inventory optimization that adapts to demand variability
Most inventory tools size each node in isolation — DC stock optimized one way, store stock optimized another, and the central warehouse stock is disconnected from both. The result is a duplicated buffer at every echelon and aggregate working capital that is structurally higher than service levels actually require. TrueGradient solves the multi-echelon problem natively. The platform optimizes inventory targets jointly across DC, warehouse, store, and channel, propagating demand variability and lead-time variability up the network and right-sizing buffer at the echelon where it is most cost-effective.
Dynamic safety stock engineered to your service level targets
Legacy inventory tools calculate safety stock with static multipliers — a fixed number of days of cover, or a coefficient derived once and rarely revisited. That breaks the moment demand volatility shifts, lead times stretch, or a new SKU enters with a different demand profile. TrueGradient calculates safety stock dynamically from the full probability distribution of forecast error and lead-time variance per SKU per node.
Agentic AI for exception-based inventory management
TrueGradient agents continuously monitor the full SKU × location portfolio across three layers — observation, decision, and action. Stockout-risk agents flag SKUs where forecast error trajectory threatens upcoming service levels. Excess-inventory agents flag positions where holding cost is materially above optimum. Reorder agents propose corrective replenishment orders with full attribution — why the order is needed, what risk it covers, what working capital it commits. Your planners only see the exceptions that warrant intervention. Routine inventory decisions are handled by the system.
Built for the operating reality of supply chain planning teams
Inventory optimization in the real world is not a clean mathematical exercise. Lead times shift. Supplier reliability is uneven. Finance pressures working capital one quarter and service levels the next. New SKUs launch with no history. Promotions distort demand. Stockouts in one channel cascade into another. TrueGradient is built around that reality.
Explainable recommendations
Every inventory recommendation surfaces the drivers behind it, so planners defend, override, or accept with confidence. No black box.
Continuous learning
The model retrains on inventory outcomes and override decisions, so accuracy compounds cycle over cycle.
No-code, self-serve
Planners configure service-level policies, run what-ifs, and adjust constraints without data-science support or a six-month implementation queue.
The business impact of AI-powered inventory optimization
Better inventory optimization compounds through every downstream P&L line. With TrueGradient, consumer brands and retailers consistently see:
15–30% reduction in inventory and working capital at constant or improved service level
30–50% reduction in stockouts through demand-aware safety stock and continuous re-planning
20–40% reduction in excess and obsolete inventory exposure via probabilistic sizing and exception-based monitoring
Days, not quarters to first inventory recommendation — typical TrueGradient deployment in 30–90 days vs. 12–18 months for legacy APS platforms
Every percentage point of working capital released compounds against your cost of capital. Every prevented stockout protects an account relationship. The same model handles both — without the trade-off that legacy inventory tools force on the planning team.
“We chose TrueGradient for its AI-driven platform and deep CPG expertise. It is already boosting forecast accuracy, service levels, and logistics efficiency.”
Kisah: minimized stockouts
TrueGradient has played a pivotal role in driving seasonal sales for Kisah by minimizing stockouts with accurate demand predictions and stock planning.
Read case study →Vastramay: working capital optimization
An apparel brand reduced working capital exposure and optimized inventory levels using TrueGradient's AI-native inventory optimization platform.
Read case study →Connect inventory optimization to the rest of your planning stack
Get Clarity Before Your next
planning cycle
See what AI inventory optimization software looks like on your data. Run TrueGradient in parallel with your existing process and measure the working capital and service-level lift in your first planning cycle. No rip-and-replace, no months-long implementation.
For Shopify Brands
Optimize inventory, prevent stockouts, and boost profits.
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TrueGradient is a no-code self-serve AI product for supply chain optimization founded in 2023.
