Optimize revenue decisions with vertical AI for retail.
Wavemind builds specialized agents and predictive systems for fashion and consumer retail — improving margin, inventory rotation, and planning with measurable outcomes.
Scenario A
30% discount / 3 weeks
Margin: 42.1%
Scenario B (Recommended)
20% discount / 5 weeks
Margin: 48.7%
Gross Margin
48.7%
+6.6pp
Sell-through
87%
+12%
Inventory Turn
4.2x
+0.8x
Revenue Trend (12W)
Reduce discount depth, extend full-price window. Est. +$140K margin uplift.
Integrates with
The Problem
Retail markdown and demand planning are still driven by intuition.
Margin erosion
Suboptimal discounting strategies destroy margin across seasons, with no feedback loop to improve.
Over-inventory pressure
Excess inventory ties up working capital and forces aggressive markdowns at end-of-season.
Forecast errors
Inaccurate demand forecasts cause stockouts on winners and waste on underperformers.
Products
Two products. One intelligence layer.
AI Copilot
Assists post-sale, commercial operations, analytics, and decision support — integrated with ERP, e-commerce and transactional systems.
- Post-sale workflows
- Commercial insights
- Data Q&A
- Decision support
- Integrations
Insights
Category Denim shows 18% sell-through below target
Top SKU WMN-4521 at risk of stockout in 9 days
Recommended Actions
Initiate 15% markdown on slow-moving Denim styles
Rebalance inventory: transfer 240 units to Store Group B
Sources
Revenue Optimization Engine
Markdown optimization + demand forecasting using elasticity models, simulations and ML.
- Elasticity modeling
- Scenario simulation
- Markdown recommendations
- Forecasting
- KPI tracking
Margin
48.7%
Sell-through
87%
Revenue
$2.4M
Solutions
Use cases that move EBITDA.
Markdown Optimization
Determine optimal discount depth, timing and duration using elasticity and simulation models.
Demand Forecasting
Predict demand by category, channel and region with ML models trained on your historical data.
Inventory Rotation
Improve sell-through and reduce working capital pressure through intelligent inventory balancing.
Pricing Governance
Rules-based pricing frameworks with simulation capabilities for scenario planning.
Commercial Performance Insights
Real-time dashboards and alerts for margin, revenue and sell-through across all channels.
Post-sale Intelligence
Automate post-sale workflows and extract operational insights from transactional data.
Technology
Built like enterprise software. Designed like a modern SaaS.
Models
Predictive intelligence
- Demand forecasting
- Price elasticity
- ML pipelines
Agents
Orchestrated decision-making
- Task orchestration
- Tool integrations
- Guardrails & safety
Integrations
Enterprise connectivity
- ERP & e-commerce APIs
- Data warehouse
- Custom connectors
How it works
Connect data sources
ERP, POS, e-commerce, warehouse
Model & simulate
Elasticity, forecasting, scenarios
Recommend actions
Optimized with optional execution
Measure uplift & learn
Track KPIs, iterate, improve
Why Wavemind
Not generic AI. Vertical intelligence for retail.
Vertical specialization
Purpose-built for fashion and consumer retail, not a generic horizontal tool adapted for your industry.
Quant + simulation approach
Elasticity models, scenario simulations and ML — replacing intuition with data-driven decisions.
Integration-ready
Connects to your ERP, e-commerce and data infrastructure out of the box. No six-month integration cycles.
Measurable governance & ROI
Every recommendation is tracked and measured. Built-in governance to prove value to leadership.
Proof
See what decisions look like with intelligence.
From data to decision in seconds
Wavemind connects your transactional data and generates actionable, quantified recommendations that your teams can trust.
- Scenario comparison across discount strategies
- Elasticity-informed markdown recommendations
- Forecast-assisted planning and inventory balancing
Gross Margin
48.7%
+6.6pp
Sell-through
87%
+12%
Revenue Uplift
$340K
+8.2%
Scenario A vs B — Weekly Sell-through
Scenario B delivers +$140K margin uplift with 5-week discount window. Recommend implementation for Spring '26 collection.
Pilot Program
Start a pilot in 2-4 weeks.
Week 1
Data ingestion + baseline
Week 2
Models + simulation
Week 3
Recommendations + review
Week 4
Results + rollout plan
FAQ