Data Engineering & Analytics
All your business data in one place, clean and ready for reports, forecasts and AI.
ERP, online shop, CRM, marketing platforms, IoT sensors — we build the pipelines, the warehouse and the dashboards, and run them on our cloud or yours.
DATA ENGINEERING
One source of truth instead of ten exports.
Pipelines that move data reliably, a warehouse built for questions, and dashboards people actually open.
In most companies the data already exists, it is just scattered. Orders live in the online shop, invoices and stock in the ERP, leads in the CRM, ad spend in Google Ads and Meta, machine readings in an IoT platform. Answering a simple question such as "what did we really earn per customer last quarter, after returns and marketing costs" means somebody exports five files and spends a day in a spreadsheet. The numbers rarely match between departments, and nobody quite trusts them.
We build the layer that fixes this: automated pipelines that collect data from every system, a warehouse where it is cleaned, joined and kept with full history, and dashboards on top. Our team has been writing production software since 2013, including integrations for ERPs and online shops, so we know where source data tends to be messy. The same foundation later feeds process mining, machine learning and AI assistants that answer from your own data.
Pipelines and integration
ETL and ELT jobs that pull from APIs, databases, files and event streams on a schedule or in near real time. Incremental loads, retries and alerts, so a failed sync is noticed in minutes, not at month end.
Warehouse and data quality
A data warehouse or lakehouse sized to your volume: PostgreSQL for moderate data, ClickHouse for billions of rows, DuckDB and Parquet for cheap analytical storage. Automated checks catch duplicates, gaps and values that suddenly look wrong.
Dashboards, forecasts and AI-ready data
Dashboards in Metabase, Power BI or Grafana with metrics defined once and used everywhere. On the same data: demand and cash-flow forecasts, features for machine learning models and documents prepared for retrieval by AI.- Data source inventory and a map of how data flows today
- Connectors for ERP, e-commerce, CRM, marketing and IoT
- Scheduled and near real-time ETL/ELT pipelines
- Data warehouse or lakehouse with full history
- Tested SQL models with one definition per metric
- Automated data quality checks and alerts
- Dashboards for management, sales, operations and finance
- Forecasting and machine learning features
- Data and documents prepared for AI and RAG
- Hosting, backups and monitoring on our cloud or yours
HOW WE WORK
From scattered exports to trusted numbers
Start with the questions that matter, deliver something useful early, then extend.
Questions and sources
We sit down with the people who need the numbers and list the decisions they make. Then we map which systems hold the data, how it can be accessed and who owns it.
Architecture proposal
A short written proposal: storage engine, pipeline tooling, refresh frequency, hosting location, access rules and an estimate. Sized to your data volume, not to a trend.
First pipelines
We connect the two or three most important sources first and load their history, with quality checks from day one. Usually this is the ERP or shop plus one more system.
Models and metrics
Raw tables become clean, documented models: customers, orders, products, margins. Every metric gets one agreed definition, so sales and finance finally see the same number.
Dashboards and handover
We build the first dashboards together with the people who will use them, then document everything and train your team to explore the data on their own.
Operate and extend
We monitor the pipelines, add new sources as the business changes and build on top: forecasts, machine learning models, AI assistants. Engagement can be fixed-scope or value-based.STACK AND RISKS
Boring, proven tools. Careful handling of your data.
We pick the simplest stack that handles your volume. Many companies are well served by PostgreSQL and a few well-written SQL models. When data grows into hundreds of millions of rows, such as clickstreams, sensor readings or detailed order lines, we use ClickHouse or a lakehouse on Parquet files queried with DuckDB. Pipelines are written in Python and SQL and orchestrated with tools like Airflow or Dagster; dashboards run in Metabase, Grafana or Power BI if your company already uses it. For systems that need two-way synchronisation rather than reporting, see ERP integration.
The risks we watch for are the usual ones: source systems slowed down by heavy extraction, silent schema changes that break reports, personal data copied where it should not be, and dashboards that disagree with the accounting system. We read from replicas or off-peak, validate schemas on every load, mask or exclude personal data by default, and reconcile key totals against the source. Everything can run on our own cloud and network in Romania (AS56951), on GPU servers when models are trained, or entirely inside your infrastructure; for sensitive workloads see private AI.
FAQ
Questions we hear often
Do we need a data warehouse, or are a few reports enough?
If your questions only touch one system, its built-in reports are often enough. A warehouse pays off when answers need data from two or more systems, when history matters (the ERP keeps only the current state), or when exports and spreadsheets are being stitched together by hand every week. We will tell you which case you are in.
Which systems can you pull data from?
Anything with an API, a database we can read, file exports or a message queue: ERP and accounting systems, Magento, WooCommerce and other shops, CRMs, Google Ads, Meta and GA4, payment providers, IoT gateways and MQTT brokers. For older systems without an API we read from database replicas or scheduled exports.
Where is our data stored and who can access it?
Where you decide: on our own cloud in Romania, on your servers, or in your existing cloud account. Access is role-based, personal data can be masked or kept out of analytics entirely, and every pipeline is documented so you know exactly which data moves where.
Will we be locked into your tools?
No. We prefer open formats and open-source components such as PostgreSQL, ClickHouse, Parquet and dbt-style SQL models. The code lives in your repository, and the warehouse can be operated by your own team or another provider later.
How does this connect to AI projects?
Most AI projects stall on data, not on models. A clean, documented warehouse is the input for forecasting, machine learning features and assistants that answer questions from your own documents and figures. We prepare that layer first, so the AI work that follows has something reliable to stand on.
Data everywhere, answers nowhere? Talk to an engineer.
Tell us which systems you use and which questions you cannot answer today. We will tell you honestly what it takes to get there.