Build
A data warehouse
One place where data from all your systems is kept, with its history, organised so a new system next year doesn't mean rebuilding today's reports.
Data platforms · Practical AI · Sweden
Reliable data, made easy and accessible.
Most companies feel they should be doing more with AI, and few have the data to make it work. BackenData builds that foundation — a data warehouse, automated pipelines and dashboards your team trusts — then connects AI to it. Practically, without the hype.
Automated pipelines and a working data warehouse,
with the first parts running in weeks, not months.
How we get you there
step by step
Assess
Where AI will pay off
A short, honest look at your data, systems and ideas, ending in a plan for what to do first — and what to leave alone.
Foundation
Data you can rely on
A data warehouse fed by automated pipelines, so figures from every system land in one place, checked and up to date.
Connect
AI that knows your business
Connectors that let AI assistants look things up in your own systems, with only the access you choose to give.
Run
Kept working
Monitoring, alerts and regular checks, so what was right in January — reports, models, assistants — still is in October.
Services
Get the data right. Then put AI to work on it.
Models are only as good as the numbers behind them. So we work from the foundation up — and if yours isn't ready, we will say so before selling you AI.
Automate
Automated pipelines
Scheduled jobs that copy data from each system into the warehouse — checked against the source, safe to re-run, and quick to raise an alert when something is late.
Show
Custom-built dashboards
Built around the decisions your team makes, on one agreed set of key figures, in Power BI or a simple web app — with any number openable to see what it is made of.
Connect
MCP servers & connectors
MCP (Model Context Protocol) is the open standard AI assistants such as Claude, ChatGPT and Copilot use to reach other systems. We build connectors that let them work with yours.
Predict
Forecasting & machine learning
Demand and revenue forecasts, customers likely to leave or buy again, documents read automatically — built on your own history, shown in the dashboards you already use.
Adopt
Making good use of AI
Choosing where AI is worth it, setting clear rules for what it may see and do, and showing your team how to use it — so it becomes part of the work, not another stalled pilot.
Experience
The kinds of data we work with.
Marketing & advertising
Ad spend from platforms that each count their own way, linked to real sales — so attribution rests on revenue, not on what each platform claims.
Finance & sales
Revenue, cost and margin by product, customer, market or currency, in as much detail as needed — and kept in step with the accounts.
Sensors & machine data
High volumes of readings arriving continuously from equipment, stored in full and summarised into figures people can use.
Operations
Orders, stock and deliveries from several platforms, matched into one view of customers and products, even when each system names them differently.
Tools we work in daily — chosen to fit what you already run
Databases & warehouses
- Azure SQL / SQL Server
- PostgreSQL
- Snowflake
- StarRocks
- Synapse & ClickHouse
Cloud & automation
- Microsoft Azure
- AWS
- Kubernetes & Terraform
- Apache Airflow
- Azure Data Factory
AI & delivery
- Python & SQL
- scikit-learn, XGBoost & LightGBM
- Claude & OpenAI APIs
- MCP servers
- Power BI & web apps
Approach
Boring infrastructure is the goal.
Data systems fail quietly: a job that loads nothing still reports success. Most of our work is making that impossible — for reports and AI alike.
Every number can be traced
Any figure on a dashboard or in an AI answer should lead back to the transaction it came from. If it can't, the work isn't finished.
AI sees only what it should
Assistants get the access a person in that role would have, no more. You decide what they can read and what, if anything, they may change.
You own what we build
Documented, readable work your team can pick up and extend after hand-over. No black boxes with our name on them.
Three ways this usually starts
Review
A short, focused look at your data and your AI plans, ending in a written list of what is fragile, where AI would pay off, and what to do first.
1–2 weeks, fixed scopeBuild project
A defined piece of work from start to finish — a data warehouse set up, reporting rebuilt, or AI connected to your systems — with the old setup kept running until the new one is proven.
Project-basedOngoing support
For companies with real data needs but no data team of their own: a set number of days each month keeping things running and moving forward.
Retainer, days per monthContact
Tell us what your numbers aren't doing.
A few lines are enough to start — a report nobody trusts, or an AI idea that never got past the pilot. If it isn't something we are the right fit for, we will say so.
contact@backendata.com