Management dashboard
A unified view of sales, inventory, collections, and cost with single metric definitions and a known data source.
AI services / data
AI data analytics and management dashboards
From scattered spreadsheets to a trustworthy dashboard, timely alerts, and scenarios that show their assumptions.
From reports to decisions
Most organisations do not lack data; they lack trustworthy, timely data. This service starts with the decision question rather than the tool: who makes which decision, how often, and which data must be available and correct at that moment.
Common use cases
Sales and demand forecasting
Forecasts with uncertainty ranges, backtested on your own history, instead of one certain number.
Alerts and anomalies
Flag unusual patterns in transactions, consumption, or quality for human review.
Customer analytics
Segmentation, churn, and customer value with clear definitions and controlled campaign tests.
Ask in Persian
Persian Q&A over organisational data, showing the executed query and the source of every number.
Cash flow scenarios
Scenarios built from traceable inputs with visible assumptions, alongside the finance team.
How we work
Data quality and access are assessed first. A forecasting model or advanced analysis is added only once the base data is reliable.
- 01
Decision question
The decision, owner, cadence, and cost of a wrong call are documented.
- 02
Data audit
Sources, quality, gaps, and conflicting metric definitions are identified.
- 03
Data layer
Cleaning, integration, and single metric definitions on top of current systems (accounting, sales, warehouse).
- 04
Views and alerts
Dashboards and alerts for that decision, with access levels and a source for every number.
- 05
Models and scenarios
Where needed, forecasting or scoring is added with backtesting and error reporting.
Design controls
- One definition per metric
- Every metric has one owner, one formula, and one source so two reports never show two different numbers.
- Number lineage
- Every dashboard number is traceable to its query and base data.
- Uncertainty
- Forecasts are reported with ranges and backtest error, not as certainty.
- Accountable human
- Analytics output is a suggestion or alert; decisions and actions stay with the responsible person.
Zharf
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Frequently asked questions about data analytics
How is this different from a business intelligence tool?
A BI tool builds views; this service designs the decision question, metric definitions, data layer, and where needed a forecasting model, and it can run on your existing tool or a lightweight layer. Tool selection follows the data assessment.
Our data is spread across spreadsheets and several applications. Is it usable?
Yes, but the data audit determines which sources are reliable and which gaps must be filled before a dashboard. Most projects get the most value from integration and single metric definitions rather than from a complex model.
Are sales forecasts reliable?
Reliability is measured by backtesting on your own history, and the useful horizon and forecast error are reported. We do not present forecasts without uncertainty ranges and a comparison with the current planning method.
Can we ask questions about our data in Persian?
Yes, Persian Q&A over data can be implemented, provided the executed query and the source of every number are visible so the user can verify the answer.
Choose one decision to start with
In the first conversation we review the target decision, data sources, metric definitions, and acceptance criteria, and decide whether a data pilot is meaningful.