AI Agentic Workflow for Portfolio Pricing (Optimizing Prices Across a Product Catalog)

Portfolio-level pricing automates the complex trade-off between maximizing individual SKU margin and total category revenue. It eliminates the operational bottleneck of siloed pricing teams making suboptimal, conflicting decisions by ingesting cross-elasticity models, inventory positions, and competitive data to calculate a globally optimal price vector. The upside is 3-7% incremental category revenue through coordinated price adjustments that steer demand between substitute and complementary products, protecting strategic market positions.

Implementation requires a central orchestrator, like LangGraph, to sequence the optimization agent, a constraint handler for margin floors and price ladders, and an approval gateway. The rollout engine must execute changes synchronously across the catalog in ERP systems like SAP or Oracle to avoid transient pricing mismatches. Controls include pre-flight simulations, anomaly alerts for unexpected cross-elasticity, and a dashboard to monitor category-level KPIs like revenue share shift and margin dilution post-deployment.

Portfolio pricing automation eliminates the margin leakage and cannibalization that occurs when SKUs are priced in isolation. The workflow ingests demand forecasts, competitor moves, and historical substitution data to model how a price change on one product impacts sales of others. A multi-agent system then solves for the optimal price vector across the catalog, subject to margin floors, inventory positions, and strategic guardrails. This shifts optimization from individual item profit to total category contribution, directly improving revenue capture and strategic growth.

Implementation requires a LangGraph or custom orchestration layer to sequence agent tasks, manage state, and handle exceptions. The Optimization Engine, often a linear programming or reinforcement learning model, runs within a governed sandbox. Price updates are batched and pushed to systems like SAP, Oracle Retail, or a PIM via resilient connectors. Critical controls include pre-flight margin checks, simulated impact analysis, and a mandatory review gate for high-risk or high-value changes. Observability dashboards track portfolio KPIs, substitution rates, and agent decision rationale for continuous tuning.

Phase 1 establishes the data foundation and governance layer. We integrate with your ERP (SAP, Oracle) and PIM to create a clean, real-time product catalog feed. Concurrently, we deploy the rule engine and approval gate architecture within a platform like LangGraph, defining margin floors, competitive guardrails, and human escalation paths for all price changes. This phase delivers a controlled, audit-ready environment where initial pricing logic can be tested safely without impacting live systems.

Phase 2 integrates the portfolio optimization core. We deploy and calibrate the cross-elasticity model, which analyzes substitution and cannibalization effects across SKU categories. The LangGraph orchestrator now uses this model, alongside the Phase 1 rules, to generate coordinated price change bundles. These bundles are validated through the existing governance gates and executed in a controlled rollout to a subset of products, with close monitoring of category-level revenue and margin impact before proceeding to full-scale autonomous execution in Phase 3.

Portfolio-level price optimization automates the complex trade-off between individual SKU margin and total category revenue, a calculation impossible at scale with manual analysis. The workflow ingests cross-elasticity models, inventory positions, and competitive data to generate a set of interdependent price changes. The operational upside comes from preventing revenue leakage through substitution and strategically steering customer flow within the catalog, directly impacting gross margin and market share. Implementation requires tight integration with product master data and pricing rule engines.

Rollout is executed in controlled phases, often by region or customer segment, to monitor for unintended substitution effects before full deployment. The architecture must include a real-time monitoring layer that tracks key metrics like basket composition and category-level revenue against forecasts, triggering automated rollbacks if cannibalization exceeds thresholds. This phased approach, governed by pre-defined business rules in systems like SAP or a custom rule engine, de-risks the automation, turning portfolio strategy into a repeatable, measurable operational process.