DATA FOR DECISIONS
Understand the data.
Check the forecast
We collect data into understandable reports and check where forecasting is useful. We set up indicators, evaluate source quality, and compare the model with the base method using historical data.
TASK
DATA
SCENARIO
PILOT
VERIFICATION
LAUNCH
forecast, data warehouse
UNIFIED METRICS
HYPOTHESIS TESTING
SOLUTIONS
projects
of studio
models
Premier Partner status
marketing, analytics
Before forecasting,
make sense of the data
A report loses meaning when sales in CRM, payments in accounting, and advertising expenses are calculated using different rules. We start with definitions: what counts as an order, a refund, and revenue, for what period, and from which source.
We check the completeness of history, duplicates, gaps, and update delays. We collect agreed sources, calculate metrics, and show limitations. Sometimes the first useful result is a stable dashboard without machine learning.
If the data is suitable for a forecast, we set a horizon and a method of verification. We compare the model with a simple baseline method on a later segment of history that was unavailable during training. We evaluate the result by the chosen error metric and applicability to the business solution.
- Dictionary of indicators and agreed sources
- Checking data quality, completeness, and freshness
- Dashboard with intuitive filters and periods
- Forecast with verification on deferred history
- Description of limitations and update procedures
Metrics
Unified calculation rules for the team
Quality
Gaps and delays are visible in the report
Verification
The model is compared with the base method
Action
Every metric has a question it answers
A forecast is an assessment, not a promise
We do not substitute uncertainty with one exact number. We discuss the range of possible values, the horizon, and conditions of application. If the model does not improve the base method, that is also a result of the research.
Analytics with verifiable foundation
From defining an indicator to the decision the team makes based on it.
Unified calculation logic
We agree on events, currencies, periods, and returns accounting. We resolve discrepancies with source systems before the report becomes the basis for a decision.
Fair model audit
For time series, we observe time order and exclude features that become known only after the event. We compare errors on identical periods.
Update policy
We set up a schedule for uploads and freshness checks. Access, placement, and data composition are agreed upon before connecting external services.
From sources to a clear solution
We add machine learning only where testing shows practical benefit.
Business question
We define the solution: procurement, segmentation, sales plan, or funnel control. We set the horizon and utility criteria.
Source audit
We check history, identifiers, and available exports. We note gaps, duplicates, and access restrictions.
Data assembly
We configure loads, transformations, and metric definitions. We reconcile the sample with source systems.
Report and basic method
We build a dashboard and a simple way to evaluate. We check which questions are already being solved without a complex model.
Forecast verification
If it is included in the package, we train the model on historical data and check it on the next period. We analyze errors and stability.
Handover and observation
We explain metrics and constraints to the team. We hand over load descriptions, update rules, and response procedures for quality degradation.
Three layers analytics system
Sources, calculations, and visualization must operate on the same logic.
Collection and storage
SQL, available APIs, and agreed-upon exports. For high volumes, we design a storage system; we choose ClickHouse or another tool based on load and hosting.
Analysis and models
Python for research, segmentation, and forecasting. We fix the error metric and verification procedure before evaluating the result.
Reports for the team
We select the BI tool based on access and data storage location. We show the period, source, update date, and indicator definitions.
— COST
Pricing
to AI analytics
From an agreed report to forecasting verification and data warehouse construction. We clarify source complexity and historical volume before starting.
| Scope of work | Basic dashboard Metrics and report from agreed sources 25 000 ₽ from 10 days | POPULAR Predictive analytics One model with historical data verification 65 000 ₽ from 20 days | Turnkey DWH Storage and analytical processes as assigned from 120 000 ₽ from 35 days |
|---|---|---|---|
|
Data sources
|
Up to 3 sources | Up to 6 sources | Composition by task |
|
Storage
|
Not included | Analytical basis for the task | Architecture and loading per assignment |
|
Forecast
|
Not included | One model and its verification | Set of tasks by assignment |
|
Customer segmentation
|
Not included | RFM if necessary data is available | Segments and updating by task |
|
Notifications
|
Not included | Schedule summary | Channels and conditions as per task |
|
Defect correction
|
14 days | 30 days | 90 days |
|
Handover to the team
|
Instruction | Instruction and one webinar | Training and a month of maintenance per task |
One-time cost of work. BI licenses, servers, cloud resources, and paid sources are paid separately. A forecast is possible with sufficient history; its quality is determined by verification, not a guaranteed percentage of accuracy.
— QUESTIONS AND ANSWERS
Everything that matters
before start
Scope of work, source materials, approval order, and launch conditions.
-
How is a report different from a forecast?
A report describes what has already happened according to agreed rules. A forecast estimates future values over a given horizon and contains an error. Applying it requires checking against history and understanding the limitations. -
What accuracy do you guarantee?
We do not promise a percentage in advance. We choose a metric for the task, compare the model with a baseline method on deferred history, and show the verification result. If the quality is insufficient for the solution, we propose a different approach or data refinement. -
What to do if the data is incomplete?
We start with an audit and fix what can be restored and what limits conclusions. With a short history or a changed process, a forecast may not be useful. In that case, we first build collection and reporting. -
Will data be transferred to the cloud?
We agree on the placement and access schema before connection. Cloud BI or another external service can receive data; a local version is designed separately. We do not promise no external transfer without checking the entire chain. -
What happens after the model is launched?
Observation of errors, control of data freshness, and periodic updates are needed. The order, frequency, and cost of maintenance are agreed upon separately; the included period is described in the selected package.