Agentic Pricing Methodology

One source of truth for every pricing decision
We unify the data that actually drives pricing outcomes—so decisions are consistent across stores and channels:

Outcome: cleaner inputs, fewer exceptions, higher trust.

• Sales, margin, stock, availability, logistics
• Promotions and markdown history
• Competitive prices and promo mechanics (incl. online & marketplaces)
• Seasonality, events, external context, weather and etc.)
• Text signals (promo descriptions, supplier terms, reviews etc.)

Outcome: cleaner inputs, fewer exceptions, higher trust.

An ecosystem of specialized models, not a single black box
‍Pricerium uses a layered set of models that evolve independently and scale across categories:

• price response & sensitivity models
• causal / uplift models for promo & markdown effects
• constraint-aware optimization components
• online learning for safe exploration
• NLP to convert text into structured signals
• anomaly and data-quality protection models

Outcome: faster iteration, better accuracy, and resilience in production.

Generates strategies and scenarios—not one fragile “optimal price”

‍Generates strategies using full contextual intelligence—not just historical curves. Instead of outputting a single “optimal price,” Pricerium’s Generative Pricing Engine produces multiple feasible pricing strategies and scenarios under uncertainty. It combines your pricing rules with contextual data—so recommendations reflect what is happening right now, not only what happened before:

• margin floors, price ladders, KVI policies
• competitor index targets, rounding, guardrails
• multi-objective trade-offs (margin / revenue / sell-through / stability)

‍Outcome: pricing that is context-aware, explainable, and resilient—because it is generated for the real market situation, not an abstract demand curve.

Simulate competitor reactions before you commit
‍Pricerium models the market as actors, not curves:

• ‍Your Pricing Agent proposes actions
• ‍Competitor Agents simulate likely responses (match, selective response, escalation)
• Shopper / Market Agents model behavior across channels and context

‍Outcome: strategies that remain strong even when competitors react.

The system gets better with every cycle
‍Pricerium operationalizes continuous improvement:

• governed rollout (human-in-the-loop by default)
• experimentation (A/B, geo tests, controlled exploration)
• learning from real outcomes (uplift, margin impact, cannibalization)
• model and agent updates through a controlled pipeline

Outcome: compounding gains, not one-time “recalculation” projects.