Forecasting and analytics
We turn the data you already collect into forecasts and signals your team acts on, from orders to retention and risk, and keep the models accurate after launch.
When you need this
- Years of data, but decisions still rest on instinct
- You learn that customers left only after they have gone
- Purchasing and capacity are planned in spreadsheets
- You suspect losses or fraud but cannot pin them down
Related example
Demand forecasting for a retail chain
Every week, each store gets a forecast per product that allows for promotions, holidays and the season.
- Data
- sales, stock, prices and promotions
- Forecast
- per product and store
- Order
- a suggestion managers can adjust
What is included
- Forecasts of demand, churn, load and revenue
- Scoring of applications, leads and risk
- Recommendations for stores and digital services
- Anomaly detection for fraud, failures and process drift
- A data audit and dependable preparation pipelines
- Monitoring and retraining once the models are live
How we work
From the first call to support
Step 01
Audit
We study the process, data and systems, and agree what success will look like.
Step 02
Prototype
A technical prototype proves the approach on your real data in three to six weeks.
Step 03
Development
Changes ship as they pass review and tests, and you follow them live on TaskDeck.
Step 04
Integration
The system joins your infrastructure, next to everything that already works.
Step 05
Support
Three months of warranty, then a support and development plan.
After launch
We fix defects for three months after delivery, under warranty. Ongoing support, monitoring and further development are agreed separately, in a plan that suits you.
Questions and answers
How much data do we need?
It depends on the task. A data audit at the start shows what is usable, what is missing and whether the forecast is worth building at all.
How do we know the model works?
We test it on a period it has never seen and compare it with the way you forecast today, using measures agreed before the work starts.
Where do the results show up?
In the systems people already use: an order screen, the CRM or a dashboard, rather than yet another tool to open.
What happens when the data changes?
Accuracy is monitored after launch, and the model is retrained on a schedule or as soon as quality drops.
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Get a preliminary assessment
Describe the task. Once we have read it, we will suggest a call and the materials to bring.
- An NDA before you share anything
- A free assessment, with no obligation
- A reply within one business day
Or write to hello@ampliative.ai