[ LEADERSHIP ]
Meet the Team
Jeff Alpert
Founder & CEO
Aldo Marini
Founder & CTO
Stop Guessing. Start Knowing.
Traditional systems plan to a single number. Pillar AI shows you the full probability distribution, predicting outcomes across thousands of scenarios. Cut up to 20% working capital while improving service.
Get a DemoSingle-point forecasts can't handle variability. Planners inherit safety stock targets from backward-looking averages, then firefight when reality diverges. Stockouts and excess inventory are symptoms of the same disease: deterministic planning in a probabilistic world.
We model the full probability curve, not just point estimates. Demand has a distribution. Supply has a distribution. Inventory compounds both. We quantify the probability of stockouts and excess before they happen, then recommend corrections that maximize your win:loss ratio.
Unlock full visibility. Place Better Bets. Our artificial intelligence unveils the concealed potential in your supply chain data.
Free up millions in working capital while maintaining or improving service levels. Stop tying up cash in excess inventory.
Dramatically reduce outlier inventory events, both very high (excess) and very low (stockout risk), through proactive corrections.
Reduce out-of-stock and lost sales events by identifying stockout risks before they happen and recommending preemptive action.
Every inventory position is a bet. We quantify the odds: "Your current inventory has a 65% chance of creating excess stock." Then we improve the bet mathematically.
Our AI-powered system creates a unified data model that ingests and harmonizes data from various sources, running thousands of Monte Carlo simulations.
Thousands of SKU-node level recommendations per week, sorted by monetary impact. The system catches what humans can't.
Tune aggressiveness based on your preference: more inventory reduction vs. more service protection.
Our transformer-based AI outperforms traditional ML models on point forecasts and produces well-calibrated probability distributions.
Our AI models continuously learn from changing patterns and user inputs, offering real-time updates to forecasts and recommendations.
Traditional planning makes blind bets, leaving planners to guess at the odds and make adjustments based on feel. We tell you the actual odds and improve your bets mathematically.
Your current inventory position is a bet you've placed on future demand. We calculate the probability distribution of outcomes for that bet. You see the odds of stockout vs. excess before you act.
When the odds are unfavorable, we recommend adjustments to improve the bet. A "win" means the adjustment prevented a stockout or excess event. A "loss" means the adjustment was unnecessary or insufficient. We only recommend adjustments where the expected value is strongly positive.
Our corrections win 4-5 times for every loss. This means for every correction that doesn't pan out, four or five others successfully prevent costly inventory problems.
This ratio isn't theoretical. It's measured from real corrections across our customer base. We track every recommendation and its outcome, continuously improving the model based on actual results.
We transform supply chain guesswork into quantified probabilities you can act on.
Not a single forecast, but a full probability curve. Our AI runs thousands of Monte Carlo simulations to map every possible demand outcome.
Lead time variability, supplier reliability, yield rates. We model the full distribution of when and how much arrives.
When demand and supply uncertainty combine, the result is a complex inventory distribution with stockout risk on the left and excess risk on the right. Traditional safety stock can't cover these tail risks. We quantify them.
Shift inventory right to minimize stockout probability. Ideal when lost sales cost more than carrying inventory.
Shift inventory left to free up cash. Accept slightly higher stockout risk in exchange for major capital reduction.
Tighten the distribution to reduce extreme outcomes on both ends. Minimize surprises, stabilize operations.
Real examples showing how we reduce stockout and excess events by quantifying probabilities and recommending optimal adjustments.
Bimodal distribution with inventory significantly exceeding max target. Correction reduces working capital while maintaining service.
Highly concentrated low-inventory distribution with high stockout risk. Correction brings stock to safe levels while avoiding excess.