If you generate data every day — whether in a clinic, a production line, or a store — that same data can make your decisions better.
Every day your organization generates data — hospital scheduling, a factory production line, store sales, or a logistics delivery route. I take this raw data, find its patterns, and show you exactly where you're losing money and where opportunity is hiding — no guesswork, just models and numbers.
Reduce patient wait times and staff better by forecasting visit patterns
Reduce production line downtime with optimized scheduling models
Prevent stockouts or overstock with accurate demand forecasting
The shortest, cheapest distribution route through network optimization
Analyze trading patterns with deep learning models
If you have data but don't know what decisions to build from it, this is where we start
Depending on the state of your data and your goal, one or a combination of these is delivered.
If your data is scattered across spreadsheets, notebooks and disconnected tools, we first build a proper place to keep it.
Once the data is in order, we move to patterns and prediction — with a model sized to your data, not to fashion.
A model inside a notebook has no value. The output is something your team uses every day.
Project cost depends on scope, data volume and mutual agreement. An initial estimate is provided after a free review session.
A review of your organization's data (sales, inventory, scheduling, production, resources, market) and building forecasting and optimization models that show exactly where costs are being added and how better decisions can be made.
These findings can feed directly into content creation or automation — see the next step of the Homat path →
Farrokh Ahmadi — PhD in Management · AI · ML · DS