What we do
Forecast what you need to plan. Demand for stock, revenue for the budget, capacity for servers or staff. We compare simple methods against more complex ones on your own history and keep whichever is more accurate. You get a range with a low and a high case, and a monthly check against what really happened.
Understand your customers. Segmentation based on what customers do, not just who they are. Churn and retention analysis by cohort, so you can see whether newer customers stay longer than older ones and what the ones who leave have in common. The output is a short list of findings your teams can act on, plus the queries behind them.
Run experiments you can believe. We help design A/B tests before they start: the metric, the sample size, the minimum effect worth detecting, and how long to run. Then we read the results with honest statistics, including when the answer is “no measurable difference”. A test that tells you nothing changed is still useful.
Put models into production, and watch them. When a model clearly beats a good chart or a simple rule, we ship it properly: versioned code, a scheduled or API-based scoring job, and monitoring for input drift and accuracy over time. A model that silently degrades is worse than no model, so it gets alerts like any other production service.
Tell you when you don’t need us. Plenty of questions sold as data science are really a reporting problem: the data exists, but nobody has put it in one place or defined it clearly. When that is the case, we say so and point you to a dashboard or a SQL query instead. It is cheaper, faster and easier for your team to keep running.
How it usually goes
The usual first step is the two-week data health check. Models are only as good as the data they learn from, so we trace the inputs first and find where they drift, duplicate or go missing. Sometimes that alone answers the original question.
Then we take one question at a time, agree on how success will be measured, and deliver the analysis or model in two to four weeks. Code lands in your repositories, written so your team can rerun and extend it.
A good fit if
- You plan stock, staff or servers on gut feeling and it keeps going wrong.
- You run A/B tests but aren’t sure the results are real.
- Customers leave and you don’t know why, or which ones are next.
- Someone built a model once, and nobody knows if it still works.