A hybrid pricing brain for hybrid retail

Beyond “Forecast → Optimize”

A next-generation pricing platform that combines data, models, and AI agents into a single, manageable loop of “decision → action → fact → learning.”

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4 Technological Pillars

What makes Pricerium a next-generation pricing platform

Pricing platform and data lake

A single repository for all data on prices, promotions, competitors, and sales. A modular ML engine for all scenarios.
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→ A single source of truth for prices across the entire network (online + offline)
→ Any new scenarios on the same platform
→ Easy to scale to new regions and channels

Self-learning loop and experiments

→ A single source of truth for prices across the entire network (online + offline)
→ Any new scenarios on the same platform
→ Easy to scale to new regions and channels

Pricing Copilot

AI pricing agents (Pricing Copilot)Natural language dialogue assistants on top of all models and data.
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Category managers spend time on decisions, not data collectionReduced dependence on Excel magicIncreased adoption — easier to work with, lower entry threshold

Pricing that reflects the market, not just internal data

Working with external contextTaking into account competitors' promotions, reviews, supplier conditions, market news.

Pricing based on real market contextQuick response to competitors' promotionsEasier to make strategic decisions

Platform architecture

Multi-level architecture with AI agents at the top

Agentic Layer

LLM core + tool API + specialized agent roles. Operate through official platform services.

ML engine and optimization

Demand, elasticity, causal effects of promotions, optimization, dynamic pricing, anomalies.

Pricing data lake

Unified storage of numerical and text data: sales, balances, promotions, competitors, reviews, contracts.

Integrations and API

Connection to ERP, e-commerce platforms, marketplaces, BI systems, competitive data parsing.

How it works

Trust and control at the enterprise level

Human-in-the-loop

Humans define the strategy and "guardrails," agents propose scenarios based on data and market context, and the resulting facts of these actions are fed back into the self-learning loop to refine future predictions

Audit and tracing

For each decision, the following information is stored: who made the decision, when, based on what data and rules.

Strict separation

Where is the “opinion” and where is the “formula” — mathematics in the ML layer, agents explain and combine.

Bring your toughest pricing questions?

Our experts will help you find the best solution for your needs.

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