Most tools recommend.
Ours closes the loop.
Every pricing tool stops at a number a human has to approve. Elastly sets the price, watches the market respond, measures whether it was right, and relearns, so the engine prices better every cycle.
A price isn’t a guess. It’s a dependency graph.
Elastly decomposes every price into the signals that drive it, terminating in one objective: expected earnings. Every term is causal, traceable, and graded.
You decide what
“optimal” means.
Most tools quietly optimize for margin or revenue and never tell you which. Elastly makes the objective first-class: dial the blend per product group and see the trade-off before you commit.
Three layers, increasing sophistication.
Rules carry the early value and never get bypassed. Estimation adds prediction. Optimization closes the loop. ML always proposes inside the rules, never around them.
Rules engine
Deterministic and explainable, no training data needed. The constraints every recommendation lives inside, where ML proposes within them and never around them.
Demand and sensitivity estimation
The predictive core: it routes each SKU to the right method by how much data it has, and scores every estimate for confidence.
Optimization and autonomous learning
Turns estimates into the profit-optimal price under constraints, then improves itself from realized outcomes via bandits and reinforcement learning.
No SKU is too sparse to price.
Most tools split crudely: elasticity for bestsellers, guesswork for the rest. Elastly routes every SKU to the right method by how much data it has, and the long tail borrows statistical strength from products like it.
Open loop stops. Closed loop compounds.
Everyone else hands a human a number and forgets it. Elastly measures the outcome of every price and feeds it back: the system that learns from its own results is the one that keeps winning.
Autonomy is earned, never assumed.
The loop never goes live before it proves itself in shadow. Each segment graduates from watching, to acting within a band, to full autonomy, and every move stays bounded and reversible.
Shadow mode
The engine recommends and logs what it would do. Nothing goes live until shadow prices would have beaten the actuals.
Gated mode
Auto-applies inside a tight band on approved segments, typically the low-stakes long tail first. Every move logged and reversible.
Full mode
Auto-applies within guardrails on proven segments. The human sets policy and bounds, not individual prices.
Every price carries its own reasoning.
Three things ship with every recommendation, so a CFO can trust the number and a pricing manager can reason about it, not just read it.
Explanation
The rules and weighted drivers that produced the price, in plain language.
Confidence
A data-quality score telling you how much to trust it, and what data would sharpen it.
Counterfactual
See how the price moves if you change a rule, driver, or objective, before you commit.
See the algorithm reason on your catalog.
Connect a data sample and watch the first explainable, confidence-scored recommendations land within 48 hours.