Vijan.AI
Case StudiesRetail & E-Commerce& Promotions

Retail & E-Commerce

Dynamic Pricing & Promotions

4 autonomous agents optimize pricing and promotions in real-time. 18% gross margin improvement.

4 Autonomous Agents18% Margin Lift
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Agentic AI Workflow

4 agents run in parallel to analyze margins, forecast demand, personalize promotions, and ensure tax compliance

The Challenge

Static pricing and blanket promotions were eroding margins without driving incremental volume

A multi-category online retailer with 200K SKUs was updating prices quarterly based on cost-plus models. Competitors changed prices multiple times daily, causing the retailer to be overpriced on price-sensitive items and underpriced on products with low price elasticity.

Promotions were applied as blanket discounts across categories, with 40% of promotional spend going to items that would have sold at full price. The merchandising team had no way to measure incremental lift per promotion. Gross margins had declined 3 points over 2 years despite growing revenue.

The retailer needed real-time competitive pricing, demand-curve-based optimization, and promotion effectiveness measurement.

The Solution

Agents that monitor competitors, model elasticity, set prices, and evaluate promotions

Vijan.AI deployed 4 autonomous agents. The Competitor Scraper monitors pricing across 15 marketplaces and competitor sites for all 200K SKUs. The Elasticity agent models demand curves per SKU using historical sales data, competitor pricing, and seasonality. The Pricing agent sets optimal prices via PIM API, balancing margin targets against competitive positioning and demand elasticity. The Promo Evaluator measures incremental lift per promotion, identifying which discounts drive new sales vs. cannibalize full-price demand.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Margin

Margin Analysis Agent

Real-Time Margin Analysis

Calculates product margins, price elasticity, and optimal pricing to maximize profitability.

Input

Cost data, current prices, sales volume, competitor prices

Output

Margin reports with pricing recommendations

  • Calls margin calculation tool to compute gross margin per product and category
  • Calls elasticity modeling tool to predict sales impact of price changes
  • Autonomous decision: recommend price increases for inelastic items, discounts for excess inventory
  • Routes margin insights to pricing engine for dynamic price updates

Step 2 · Demand

Demand Forecasting Agent

Demand-Based Pricing Optimization

Adjusts prices dynamically based on demand forecasts, inventory levels, and competitive positioning.

Input

Demand forecasts, inventory levels, competitor pricing, market trends

Output

Dynamic price adjustments

  • Calls price sensitivity tool to model demand response to price changes
  • Calls competitor tracking tool to monitor market prices and positioning
  • Autonomous decision: increase prices during high demand, markdown slow-moving inventory
  • Routes pricing recommendations to promotion and tax compliance agents

Step 3 · Personalization

Personalized Promotions Agent

Personalized Promotion Engine

Generates targeted promotional offers tailored to customer segments, purchase behavior, and price sensitivity.

Input

Customer segments, purchase history, promotion budget, inventory targets

Output

Personalized promotion codes and offers

  • Calls offer generation tool to create discounts, bundles, and loyalty rewards
  • Calls segment targeting tool to assign offers to high-propensity customers
  • Autonomous decision: limit discount depth for loyal customers, aggressive offers for at-risk segments
  • Routes promotions to marketing channels (email, app, site) for execution

Step 4 · Tax

Sales Tax Compliance Agent

Automated Sales Tax Compliance

Calculates sales tax in real-time based on customer location, product taxability, and local regulations.

Input

Customer location, product category, tax nexus rules, transaction value

Output

Tax amounts with jurisdiction breakdown

  • Calls nexus check tool to determine if business has tax obligations in customer location
  • Calls rate lookup tool to retrieve current tax rates by jurisdiction
  • Autonomous decision: apply correct tax rate, exempt non-taxable items (groceries, clothing in some states)
  • Routes tax data to checkout, accounting, and regulatory reporting systems

Results

Measurable impact within 90 days of deployment

18%

Margin Improvement

Gross margin improved from 32% to 37.8% through demand-curve pricing and elimination of margin-dilutive promotions.

$24M

Incremental Profit

Annual profit improvement from optimized pricing across 200K SKUs and data-driven promotional spend.

6x/day

Price Updates

Prices updated 6 times daily based on competitive moves and demand signals, up from quarterly manual reviews.

60%

Promo Efficiency

Promotional spend redirected from blanket discounts to targeted offers with proven incremental lift.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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