Sometimes the decision is already constrained by a lead time or a contract, and a better forecast would change nothing
Finding that out early saves a quarter, and we would rather tell you than build it anyway
Models built without that conversation get admired in a dashboard and ignored in practice
Every model is compared against your current method on held-out history, usually a moving average or a rule somebody wrote years ago
If it cannot beat that, we say so. A model that loses to a spreadsheet and gets deployed anyway is worse than no model
Results are reported in business units rather than in error metrics
A point forecast with no range invites planning decisions the data cannot support
Planners who see the range make better calls than planners who see a single number
Where the decision is contested or regulated we use interpretable models and ship attributions alongside
Froggik
"DESH Team maintained effective communication throughout the project."
Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the...
Viktoriia Bernatska
Co-Founder
ChainCrafters
"I liked their corporate policy and how they turned to customers and their wishes."
DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the cl...
Kolya Vovkun
CEO, Founder
Dropshipping
"I really like how they treat their clients."
DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done...
Tetyana Yarchak
CEO
For demand forecasting, two full seasonal cycles is comfortable and less is workable with weaker guarantees. For churn and risk, what matters is the number of positive examples rather than the calendar span. We check before scoping so nobody commits to something the data cannot support.
It has to beat your current method on held-out history, measured in business units. If it does not, we report that. It happens occasionally and it is a useful result, because it tells you the problem is elsewhere.
Only if they can see why it said what it said. We use interpretable models where the decision is contested or regulated, and ship feature attributions alongside predictions everywhere else. Adoption is a design problem as much as a modelling one.
Drift monitoring watches accuracy and input distributions and alerts when they move. Retraining runs as a scheduled pipeline your team owns, with evaluation gates so a bad retrain does not silently replace a good model.
Yes. Predictions land in your warehouse and surface in the tools people already use. A model that requires a new dashboard nobody opens is a model nobody uses.
