KPI changes lack context
Managers see plan-versus-actual variance but still need a consistent explanation of what changed.
Working with companies in Kazakhstan and remotely.
AI analytics for KPIs and forecasts
We build management analytics that explains KPI changes, detects anomalies, compares drivers and produces forecasts on governed business data.
KPI
metric explanations
ANOMALIES
deviation signals
DRIVERS
cause analysis
FORECAST
scenarios and outlook
The numbers changed. Why?
AI analytics supports recurring management questions with governed calculations, anomaly signals, driver analysis and forecasts.
Anomalies arrive too late
Unusual changes are found during a review instead of being flagged when the data refreshes.
Driver analysis is manual
Analysts repeatedly compare periods, segments and contributions before management meetings.
Forecasts are hard to update
Forecast logic, assumptions and scenarios need a repeatable refresh on current data.
Four practical analytics scenarios.
The analytical layer can use an existing BI model or be built with a new management analytics layer.
AI KPI commentary
Automatic explanations of trends, plan-vs-actual variance and segment contribution.
- daily and weekly insights
- plan vs actual
- management comments
- plain-language explanations
Anomaly detection
Flag unusual changes in sales, margin, inventory, requests, payments or operating metrics.
- period deviations
- segment outliers
- threshold and statistical signals
- investigation queue
Driver and contribution analysis
Compare periods, segments and factor contributions to narrow down the likely sources of a deviation.
- period decomposition
- segment contribution
- factor comparison
- evidence-linked explanation
Forecasting and scenarios
Refresh forecasts on current data and compare defined assumptions without hiding the calculation basis.
- baseline forecast
- scenario comparison
- assumption tracking
- forecast error review
Your first AI analytics project.
The scope starts from one management question, an agreed KPI definition and data that can be validated.
First stage
One KPI group and one scenario: commentary, anomaly detection, driver analysis or forecasting.
Client inputs
KPI definitions, source access, sample periods, known events, reporting rules and a person responsible for validation.
Estimate factors
Source count, data history and quality, KPI complexity, refresh frequency, validation rules, roles and deployment constraints.
Deliverables
Data and KPI specification, analytical logic, working interface or BI module, validation set, limitations and operating documentation.
Good fit
The company has recurring KPI questions, enough history for comparison and an owner who can validate the analytical result.
Not a fit
For search across corporate knowledge, document handling, request checks or controlled actions, use an AI agent.
From a question to verified insights.
Each stage keeps the calculation basis visible and the management decision with the responsible employee.
Business question
Define the KPI, decision, period and comparison that the analysis must support.
Source review
Check history, grain, completeness, refresh rules, access and known data limitations.
KPI model
Agree formulas, dimensions, comparison rules, exclusions and evidence shown with each result.
Analytical prototype
Build one commentary, anomaly, driver or forecast scenario on representative periods.
Validation
Compare outputs with source calculations, known events and expert review; record limitations.
Launch and monitoring
Connect the interface, schedule refreshes and monitor data quality, forecast error and user feedback.
Analytics or an AI agent?
AI analytics explains metrics. An AI agent works with knowledge, documents and actions.
Start with BI systems when the KPI model and dashboards are not ready. Use data integration when sources need a reliable exchange layer. Choose AI agents for RAG, document workflows, request checks and controlled actions.
Questions about AI analytics.
The analytical result must remain traceable to agreed data and calculation rules.
What tasks does AI analytics solve?
AI analytics explains KPI dynamics, detects anomalies, helps analyze deviation drivers, builds forecasts and prepares management explanations on governed data.
Are existing BI reports required?
No. An existing BI model can be used, or the work can start from preparing sources, defining KPIs and one analytical scenario.
Which systems can be connected?
BI, 1C, Bitrix24, SAP, iiko, Directum, CRM, Excel, databases and other systems can be connected as data sources.
Does AI analytics make decisions on its own?
No. It prepares calculations, signals, forecasts and explanations, while the management decision remains with the responsible employee.
Start with
your task.
20–30 minutesFree
- Review your process
- Define the first stage and next step
What to bring to the call
Name the metric, source systems, comparison period, current analysis method and who validates the result.