Tailwind

A mid-sized retailer was planning stock with spreadsheets. We replaced guesswork with a weekly forecasting system.

The challenge

Seasonality, promotions and 1,200 SKUs made manual planning impossible. Stockouts on best sellers coexisted with dead inventory.

What we did

We unified sales, stock and promotion data, built a hierarchical forecasting model and shipped a weekly planning workflow the buying team actually uses.

The outcome

Forecast error dropped by a third in the first quarter. The team now plans by exception instead of rebuilding spreadsheets every Monday.

Results

fewer stockouts

31%

fewer stockouts

less excess inventory

18%

less excess inventory

from kick-off to production

12 wks

from kick-off to production

• Python
• PyTorch
• dbt
• Databricks

A fintech could see customers leaving, but not why or who was next.

The challenge

Churn signals were buried across product, support and billing systems. Retention campaigns were broad and expensive.

What we did

We built a weekly churn-risk score, explained the drivers behind it and integrated the output into the CRM so the retention team could act the same day.

The outcome

Retention moved from mass campaigns to targeted outreach. Churn fell by a quarter within two quarters.

Results

lower churn

24%

lower churn

customer lifetime value

+11%

customer lifetime value

return on investment

3.2×

return on investment

• XGBoost
• Snowflake
• Airflow
• FastAPI

Delivery windows were static and wrong. Support was absorbing the cost.

The challenge

Routes, weather, traffic and warehouse delays were not part of the same model. Customers received a single estimate at checkout and no updates.

What we did

We combined live telemetry with historical routes, trained a gradient-boosted ETA model and served it through a low-latency API used by the tracking page.

The outcome

Late deliveries dropped by more than a quarter and support tickets about delivery times fell by 38%.

Results

fewer late deliveries

27%

fewer late deliveries

better ETA accuracy

41%

better ETA accuracy

fewer support tickets

38%

fewer support tickets

• LightGBM
• Kafka
• GCP
• Terraform

A plant was losing production hours to failures that gave plenty of warning — in data nobody was reading.

The challenge

Hundreds of sensors generated high-frequency data, but maintenance was still calendar-based.

What we did

We built a sensor-fusion pipeline, trained failure-risk models per asset and delivered alerts with the recommended action to the maintenance team.

The outcome

Unplanned downtime fell by 42% in six months, with a 96% recall on critical failures.

Results

less unplanned downtime

42%

less unplanned downtime

saved in the first year

€2.4M

saved in the first year

critical-failure recall

96%

critical-failure recall

• PyTorch
• Azure
• TimescaleDB
• Grafana