Forecast what sells.
Plan ahead.

ForecastEdge turns your sales history into a product-by-product demand forecast: what will sell next, in what quantity, and when to plan your next order.

Predicted demand · next 12 weeks
1,240 units ± confidence band
By product
prediction →
Observed
Predicted
Uncertainty band
12 weeksof demand visibility
Per SKUproducts and quantities forecast
Live inputsfrom your store or CSV
Human-ledyou approve every order

Your next stock decision should not live in a spreadsheet.

Small product teams are balancing cash, lead times, and customer demand with incomplete information. ForecastEdge shows which products are likely to sell next and the quantity to plan for.

01

Cash tied up in slow movers

Order to the demand you actually expect, with a confidence range that makes your risk visible.

02

Stockouts on best sellers

See which products are trending toward a stockout early enough to reorder while demand is still there.

03

Safety stock by instinct

Replace “just in case” with a range that shows how much buffer is enough for the pattern you sell.

From sales data to a buy plan.

ForecastEdge turns messy order history into three decisions your team can act on this week.

01

Connect your data

Upload a sales export or connect your store. Your data stays yours throughout the process.

02

See what will sell

The model learns trend, seasonality, and recent demand to build a forward view for every SKU.

03

Plan what to buy next

Get product-level quantities, timing, and buffer ranges so you can make the right stock decision at the right time.

Useful now. Honest about uncertainty.

ForecastEdge is designed to help you make a better next decision, without pretending forecasts are magic.

Production-ready today

Transaction revenue prediction

Best current model: CatBoost. Latest benchmark run delivered strong error metrics on transaction-level prediction and currently powers the MVP workflow.

In validation

Future revenue forecasting

Time-series performance is improving but not final yet. We are expanding features and evaluating with chronological and walk-forward validation.

Roadmap

Safer rollout approach

Phase 1: deploy transaction prediction. Phase 2: release forecasting as a labeled beta with confidence ranges and clear model limitations.

Built with an evidence-first modeling process

  • Model benchmarking across Random Forest, XGBoost, CatBoost, and SARIMAX rather than assuming one algorithm is always best.
  • Leakage-aware design separates transaction prediction from true future-revenue forecasting to avoid misleading accuracy claims.
  • Chronological and walk-forward validation to simulate real forecasting behavior and avoid random-split bias.
  • Transparent uncertainty reporting so decisions are made with risk context, not just a single number.
Current benchmark snapshot

MVP transaction model currently leads our internal comparison set.

CatBoost (current best)

Future-revenue forecasting remains in pilot while we improve time-series feature engineering and validation.

Built around the stock decisions that often go wrong

Three common moments where a real demand forecast changes what you'd otherwise plan on instinct.

Seasonal products

Pre-season stock planning

Know how much of each seasonal SKU to order before the season starts, with a confidence range that tells you how much buffer to actually carry.

Best sellers

Avoiding stockouts on top SKUs

Get an early warning when a fast-moving product is trending toward a stockout, in time to reorder before you lose sales.

New product launches

First-order sizing for new SKUs

Estimate initial order quantities for new products using demand patterns from comparable items in your catalog.

How ForecastEdge helps, in practice

A few examples of the kind of decisions ForecastEdge is built to support.

01
Home goods retailer

Instead of guessing how much of a best-selling item to reorder, a confidence range shows how much buffer stock actually makes sense.

02
Apparel brand

Ahead of a busy season, a per-SKU demand forecast replaces a rough spreadsheet estimate for how much stock to bring in.

03
Beauty retailer

Launching a new product, first-order quantity is estimated from demand patterns of comparable items already in the catalog.

04
Electronics reseller

Reducing overstock on slow-moving items frees up cash that would otherwise sit tied up in unsold inventory.

Pricing that grows with your Nigerian brand.

Start small, get a clear demand forecast, and upgrade when your catalog and sales channels grow.

Starter
₦25,000/mo

For small catalogs replacing guesswork and spreadsheets.

  • Up to 10 SKUs
  • 1 forecast plan
  • Shopify or CSV data import
  • Email support
Start with Starter
Enterprise
Custom

For larger teams with complex catalogs or multiple warehouses.

  • Unlimited forecasts
  • Daily refresh
  • Custom model tuning
  • Dedicated analyst support
Talk to us

Why we built this

Most inventory tools are built by engineers who bolt on a basic reorder-point formula as an afterthought. ForecastEdge started from the opposite direction: real statistical forecasting methods first, wrapped in something a small e-commerce store can actually use without a data science degree.

We believe every small retailer deserves the same demand-forecasting rigor as a large enterprise — without the headcount that usually comes with it.

Founder

Ifeoma Maryanne Ndibe

Statistician who upskilled into data science, combining classical forecasting methods with modern machine learning to bring real demand-forecasting rigor to small e-commerce retailers who've never had access to it before.

Ready to stop guessing?

Tell us what you sell and where your data lives. We will show you the clearest path to forecasting your next products and quantities.

2 Ozomena Onyeali Close, Nodu Okpuno, Awka, Anambra State, Nigeria
Response within 1 business day