AI Data Analysis Solutions
The bottleneck in analytics is almost never the model. It is that revenue is defined three different ways across four systems, and nobody agrees which figure is correct. Zelpex works on that first — getting your data into one place with defined meaning — and applies forecasting, segmentation and anomaly detection once there is something trustworthy to apply them to.
We build ingestion pipelines, warehouse models and reporting layers, then the analytical work on top: demand forecasting, customer segmentation, churn signals, anomaly detection on operational metrics. Where a well-built dashboard answers the question, we will build the dashboard and stop, rather than adding a model for the sake of it.
- Ingestion pipelines from commerce, ERP, CRM and marketing platforms
- A warehouse model where every metric has one agreed definition
- Forecasting, segmentation and anomaly detection where they change decisions
- Reporting your team can extend without an engineer in the loop
Agree the definitions
What counts as an active customer, when revenue is recognised, how returns are treated. Written down and agreed across teams, because analysis built on contested definitions gets argued with instead of acted on.
Build the pipeline
Ingestion with schema validation, tests on the transformations and alerting when a source goes stale, so a broken feed surfaces as an alert rather than as a strange-looking chart three weeks later.
Answer with the simplest tool
Many questions need a well-modelled table and a chart. We reach for a model when the question is genuinely predictive and the decision it informs is worth the maintenance.
Put it where decisions happen
Output goes into the tools people already use — the dashboard they open daily, an alert in their channel — rather than into a report that needs someone to remember it exists.
What teams ask us about data work
With the pipeline and the definitions, almost always. Teams that skip this get models trained on inconsistent data, which produce confident output nobody trusts enough to act on. The unglamorous groundwork is what makes everything after it worth doing.
For demand forecasting, roughly two years of clean history to capture seasonality, though useful results are possible with less if the pattern is stable. What matters more than volume is consistency — a catalog restructure or a channel change mid-history causes more trouble than a short series.
Yes. We generally build the warehouse and modelling layer and connect whatever you already use — Looker, Power BI, Metabase. Replacing a BI tool your team knows is rarely where the value is, and it is a distraction from fixing the data underneath it.
Where the value actually sits
In agreed definitions and a reliable pipeline. The modelling is the easy part once those exist.
We are happy to tell you that your question needs a report rather than a model. When it genuinely needs one, we build it with the same discipline as the rest of the system — versioned, monitored, and evaluated against outcomes, so you can tell whether it is still working six months later.
Thinking about a project like this?
Tell us what you are building and what is getting in the way. You will get an honest read on scope, approach, and whether we are the right team for it — including when the answer is that you do not need us.
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