Growth & Intelligence
AI-Powered Forecasting
Know what will sell, where, and when, before you buy.
We train demand forecasting models on your historical sales from every channel, adjusted for seasonality, promotions, stockouts and lead times. Forecasts are available by SKU, channel and location and feed directly into purchasing and inventory planning. Planners and CFOs get a forward-looking view instead of last month's spreadsheet.
The shift
From manual workarounds to automated by default
Before Audaxim AI
- Forecasts built by extending last year in a spreadsheet
- Peak-season stockouts on best sellers
- Cash tied up in slow-moving inventory
- Promotions and stockouts distorting sales history
- No shared forecast between ops, marketing and finance
After Audaxim AI
- Forecasts by SKU, channel and location
- Earlier warning on seasonal demand spikes
- Purchasing aligned to projected demand
- Sales history cleaned of stockout and promo distortions
- One forecast used across ops, marketing and finance
Capabilities
Everything included in AI Forecasting
Demand forecasting models
Time-series and machine learning models forecast unit demand by SKU and channel, and are evaluated against holdout periods before use.
Seasonality & event modeling
Models account for weekly and annual seasonality, holidays and marketplace events such as Prime Day.
Stockout-adjusted history
Periods where items were out of stock are detected and corrected so lost sales do not lower future forecasts.
Replenishment recommendations
Forecasts combine with lead times, MOQs and safety stock to recommend order quantities and timing for each supplier.
Inventory & cash planning
Projected purchases and inventory value roll up by month, helping finance plan working capital.
Warehouse-native delivery
Forecast outputs are written to your data warehouse and surfaced in Power BI, Tableau or Looker next to actuals.
How we deliver
A clear path from audit to always-on
- 01
Data assessment
We assess the depth and quality of your sales, inventory and promotion history across channels.
- 02
Model design
We choose forecast granularity, horizons and features based on how your team buys and plans.
- 03
Train & back-test
Models are trained and back-tested against past periods, and results are reviewed with your planners.
- 04
Deploy & retrain
Forecasts refresh on schedule, accuracy is tracked against actuals, and models are retrained as your business changes.
Works with
Your stack, connected
- Snowflake
- Google BigQuery
- AWS
- Azure
- Power BI
- Tableau
- NetSuite
- Amazon
FAQ
AI Forecasting questions, answered
How much sales history do we need?
More history helps, especially to capture seasonality, but useful forecasts can often be built with a year or more of data. For newer SKUs, we can use similar products as a starting point.
Is this a black box?
No. We share forecast accuracy by SKU group and explain the main drivers, such as seasonality and promotions, so planners can review and override when needed.
Do we need a data warehouse first?
A warehouse makes forecasting easier to maintain. If you do not have one, we can build it as part of our Data Pipelines & Cloud BI service.
How does this connect to purchasing?
Forecasts feed replenishment recommendations and can drive draft purchase orders in our procurement workflow or in NetSuite.
Pairs well with
Related services
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<30 min Daily reporting, down from 4 hoursInventory Management
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95% Inventory accuracyReady when you are
Ready to automate ai forecasting?
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