What it solves
When data is fragmented, management works in the dark.
A company does not need another export. It needs one working system. Otherwise sales, ads, logistics, finance, and documents are rebuilt by hand every time.
Data is scattered
Sales, stock, ads, finance, and documents live in different portals and files.
Numbers do not match
Without one model, management gets a new version of the truth every week.
Automation cannot start
AI, alerts, and forecasting do not work if the base is still dirty and manually assembled.
How it works
We build one system, not another report.
Data comes in from several sources, passes through orchestration, lands in storage, gets cleaned, and turns into marts for executives and teams.
Sources
We collect data from marketplaces, ERP, CRM, ad platforms, Excel, and external files.
Orchestration
We set up Airflow so loads run on schedule, failures stay visible, and history is preserved.
Storage
Raw data goes to storage. Working models and marts live in PostgreSQL and ClickHouse.
Outputs
The result is executive dashboards, finance marts, operations reporting, OCR flows, and AI tools.
If the company does not yet have the right environment, we can build it as part of the project: cloud, servers, CI/CD, Airflow, storage, and databases.
Project flow
This is serious engineering work, so we move in stages.
First audit and business goals. Then architecture, infrastructure, ingestion, migration, and only after that full analytics and AI.
Audit and business goal
We review use cases, reports, economic impact, and define where the project creates money.
Source inventory
We map ERP, CRM, marketplaces, ads, external platforms, and manual exports.
Architecture and infrastructure
We design the model and choose storage, PostgreSQL, ClickHouse, Airflow, CI/CD, and the right cloud.
Ingestion and pipelines
We build integrations, set up DAGs, land the raw layer, and verify schedules and retries.
Migration and marts
We migrate history, clean the data, normalize the model, and build marts for different teams.
BI, AI, and growth
We build dashboards, alerts, OCR flows, and prepare the system for AI and ML tasks.
Data layers
Inside the DWH, data passes through several states.
That is why the system later works for management, marketing, analysts, finance, and operations at the same time.
Raw data
API responses, Excel files, documents, and exports are stored as-is. This is your audit trail and recovery point.
Clean tables
Data is validated, normalized, joined, and turned into a stable working model.
Department marts
Separate marts appear for leadership, marketing, analysts, finance, and operations.
AI layer
We are also building tools that will make dashboard creation and system management easier on top of this structure.
Pricing
Price depends on the scale of the system.
These are working ranges. The exact number appears after the audit because data engineering always depends on source count, migration effort, and model complexity.
Source count
Two marketplace accounts are one thing. ERP, CRM, ads, external portals, and historical files are another.
Migration volume
The more legacy tables, Excel files, and history you need to keep, the longer the migration phase becomes.
Infrastructure scope
If cloud, servers, CI/CD, Airflow, and storage must be built from zero, that is a separate layer of work.
How many outputs you need
One executive mart and a full set of marts for several departments are projects of very different size.
Small business
600,000 — 1,000,000 RUB
For a fast start with a few core sources and the first management dashboards.
Mid-size business
from 1,500,000 RUB
For companies with more sources, regular loads, and data needs across several teams.
Full system
from 3,000,000 RUB
For a full migration from ERP, CRM, marketplaces, external platforms, and legacy files into one company-wide system.
Team
The project is done by people who build and implement the system.
Daniil Koveh
Architecture and delivery
Builds the architecture, explains the business logic, and gets the system into real working shape.
Daria Kiseleva
Azure and cloud delivery
Helps roll out the stack in the cloud and translates complex business requirements into real infrastructure.
Darvin
Business logic
Keeps the DWH useful for the business instead of letting it turn into a technical attraction.
Yuri
ML and forecasting
Connects forecasting and ML models once the data base is clean and the business is ready for the next step.