Stockouts and overstock at the same time
Purchasing is planned with averages: some products run out at peak times while others pile up in the warehouse.
Industries · Wholesale distribution
Data and AI for distributors: the right stock, at the right time.
We bring sales, purchasing and inventory into a single model, anticipate demand and automate tasks like order entry, so you buy smarter and stop losing sales.
We work with
The challenges we see
What holds companies like yours back
Orders entered by hand
Orders arrive by WhatsApp and email in free text and someone copies them into the system, with delays and errors.
Thousands of products and customers, little visibility
It’s hard to know which customers, products or channels make money and which don’t.
Scattered information
Sales in the ERP, e-commerce on its own and checks in spreadsheets: every department looks at a different number.
How ready is your data? Take the 2-minute AI readiness check
What we build
Solutions designed for your business
Forecasting and replenishment
A model that anticipates weekly demand and suggests what to restock before it runs out.
See the sample dashboardAI-powered order entry
An agent reads WhatsApp or email orders, enters them into your system and flags anything that is missing.
See AI solutionsProfitability by customer, product and channel
Margin and sales-mix dashboards to decide on prices, discounts and assortment.
Customer and inventory alerts
Notifications when a customer stops buying or a product is about to run out of stock.
See AI solutionsKPIs
What we measure
We define the key KPIs with your team and calculate them every day with the same rules.
- Fill rate
- Stockouts
- Days of inventory and turnover
- Overstock and slow-moving products
- Forecast accuracy
- Margin by customer, product and channel
- Average ticket and purchase frequency
- Inactive customers
- Delivery times
Demand forecast · Central warehouse
Sample data
- Horizon: 8 weeks
- Category: All
- Warehouse: Central
- Forecast accuracy
- 92.4% ▲ 6.1 pp vs. previous method
- Forecast demand (8 wks)
- 231,600 units ▲ 12.5% vs. last 8 wks
- Products at risk of stockout
- 37 ▼ 12 vs. last week
- Estimated overstock
- $2.3M ▼ 18% vs. last quarter
Weekly sales and forecast
Thousands of units
- Actual
- Forecast
- Likely range
18
22
26
30
34
Forecast
30.5K
View data
| Week | Actual (thousands) | Forecast (thousands) | Likely range |
|---|---|---|---|
| Week 1 | 21.2 | — | — |
| Week 2 | 22.0 | — | — |
| Week 3 | 21.6 | — | — |
| Week 4 | 23.1 | — | — |
| Week 5 | 22.8 | — | — |
| Week 6 | 24.0 | — | — |
| Week 7 | 23.5 | — | — |
| Week 8 | 22.9 | — | — |
| Week 9 | 24.6 | — | — |
| Week 10 | 25.1 | — | — |
| Week 11 | 24.3 | — | — |
| Week 12 | 25.8 | — | — |
| Week 13 | 26.2 | — | — |
| Week 14 | 25.5 | — | — |
| Week 15 | 26.9 | — | — |
| Week 16 | 27.4 | — | — |
| Week 17 | — | 27.9 | 27.1 to 28.7 |
| Week 18 | — | 28.3 | 27.2 to 29.4 |
| Week 19 | — | 27.6 | 26.3 to 28.9 |
| Week 20 | — | 29.0 | 27.4 to 30.6 |
| Week 21 | — | 29.4 | 27.6 to 31.2 |
| Week 22 | — | 28.8 | 26.8 to 30.8 |
| Week 23 | — | 30.1 | 27.9 to 32.3 |
| Week 24 | — | 30.5 | 28.1 to 32.9 |
Case studies
Results in the sector
Real projects, told with the numbers that matter to each client.
Do we need a lot of history for the forecast?
The more history, the better, but we evaluate it in the assessment: we review what data exists and what accuracy is reasonable to expect before building the model.
Can the agent enter orders into our system?
Yes, if the system allows it (through an API or integration). We always start with a focused pilot and with checks to review what it enters.
Where do we start?
With a data assessment: in two weeks you’ll know which sources you have and which solution to build first.
Run a distribution business? Let’s start with your data.
Tell us about your challenge and we'll map out the first step together. No cost, no commitment.