Raltera

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Do any of these sound familiar?

The following use cases illustrate typical data challenges we help organizations solve.

Every enterprise application, machine, and piece of hardware generates potentially valuable data. Most of it is never captured, never analysed, never seen.

We build the pipelines to capture that dormant operational data, structure it, and turn it into something your teams can actually act on.

CRM, finance, ops: all separate. When you need a report, someone exports everything to Excel, stitches it together, and hopes nothing breaks. It always does.

We connect your sources into one central data layer, refreshed automatically. Your reports keep working. Your team stops maintaining spreadsheets.

Sales says one number, finance says another. Two teams, two answers to the same question. Meetings turn into debates about which report to trust.

We fix the root cause, not the symptoms. One shared data layer, one set of definitions. Everyone works from the same numbers.

Your ops tool works great. Its built-in reports do not. Too rigid, too limited, and never quite answering the questions your business actually asks.

We extract the data automatically and make it available outside the tool. Custom reports that reflect how your business actually runs.

Contracts, reports, manuals, meeting notes: they exist somewhere. Finding the right one means digging through shared drives, email threads or asking the one colleague who might remember.

We transform your documents into an AI knowledge assistant. Instead of searching through files, simply ask a question in natural language and receive accurate answers with links to the original source.

The pressure to do something with AI is real. But without clean data and a clear use case, most initiatives stall in pilot mode or never leave the whiteboard.

We help you identify the right AI use cases, build the data foundation they need, and deliver the first tangible result. No hype. Just working AI.

Data done right pays for itself.

What our clients typically see when it comes together.

50-80%
less time spent on data prep
  • Reporting cycles go from days to hours
  • 40-60 hrs/week recovered for a team of 5 analysts
  • Equivalent to gaining 1-1.5 FTEs without extra headcount
  • No more manual exports or broken spreadsheets
175%
3-year ROI through compliance automation
  • Every number traceable back to its source system
  • Standardized logic eliminates human error in reporting
  • Role-based access reduces data leak exposure
  • Avoids multi-million costs from inconsistent reporting
10-15%
revenue lift through better forecasting
  • 20-30% improvement in forecasting accuracy
  • Unlocks Document AI, real-time analytics, and ML models
  • Document processing: from €10-20 down to €2-5 per document
  • Up to 500% faster document processing speed

Our approach

How we think about data engineering and why it matters for your business.

Data engineering is more than pipelines and tables.

It’s how we give data purpose. We design intelligent systems that connect, clean, and activate data so it flows effortlessly from source to decision, from decision to prediction.

Avoid small data gaps that create big blind spots.

In complex, fast-moving environments this leads to missed opportunities, delayed reactions, or inconsistent customer experiences. Our role is to close those gaps.

We build AI-ready data ecosystems.

Systems that learn, adapt, and scale. Enabling organizations to sense demand shifts earlier, act on insights faster, and personalize decisions at scale without sacrificing governance or reliability.

The result? Data that is trustworthy and fast.

Ready to fuel intelligence across your entire organization. From ingestion to transformation, from governance to analytics. We engineer data environments that think, grow, and last.

The stack we build with

Modern, cloud-native tools chosen for reliability, scale, and speed of delivery.

01 Cloud & Data Platforms The foundation everything runs on
AWS Microsoft Azure Google Cloud Snowflake Databricks Microsoft Fabric BigQuery PostgreSQL
02 Ingestion & Orchestration Getting data from source to target
Fivetran Airbyte Airflow Dagster Kafka Azure Data Factory dlt (data load tool)
03 Transformation & Modeling Shaping raw data into trusted assets
dbt (data build tool) Python SQL Spark
04 Analytics & AI Turning data into decisions and intelligence
Power BI Tableau Looker OpenAI Streamlit Snowflake Cortex

As Raltera is growing every day, so does our stack. If you can’t find the technology you’re looking for, feel free to reach out to us.

What makes us different.

Engineers who build data systems that hold up, and partners who stick around to make sure they do.

Deep expertise

Decades untangling complex data landscapes. We have seen every architecture, every legacy system, and every excuse for why the data is not ready. We know how to fix it.

People first

Great systems fail when people do not trust them. We design for adoption as much as for architecture. Your team actually using the data is the only outcome that counts.

Skin in the game

We co-build alongside your team and transfer knowledge as we go. We measure our success in your outcomes, not in delivered artifacts.

The people behind it
Johan Van Wyngene

Johan Van Wyngene

CEO & Founder

Survived Y2K and three hype cycles. Now making sure the next one lands right.

Benoit Turbang

Benoit Turbang

CTO & Founder

Fixes data problems before breakfast. Allergic to unnecessary complexity.

Bob Claerhout

Bob Claerhout

COO & Founder

Spent a decade debugging systems. Now debugs the whole organisation.

Client Stories

Real challenges. Measurable outcomes.

Gridual

Gridual

Energy Technology
Multi-vendor energy data ingestion and exposure at scale
Problem

Every energy asset partner speaks a different protocol. Each new vendor meant a one-off pipeline, slowing onboarding and putting real-time data reliability at risk.

Approach

One shared ingestion and exposure layer on AWS with thin, vendor-specific adapters on top. Ingestion, forecasting logic, and exposure kept fully separate so each can evolve independently.

Outcome

New vendor integrations plug into one shared pipeline. The team owns and extends the architecture without external dependency.

  • A blueprint that lets new vendor integrations plug into one shared pipeline instead of a new build each time
  • A clean separation of concerns between ingestion, forecasting/optimization logic and exposure
  • A common data contract defined across all sources and targets

Raltera helped us bring some structure to the natural complexities of scaling up our systems as we added more energy data sources and needed to process that data in real time. They took the time to understand the existing setup and worked through the architecture with us rather than coming in with a predefined solution. We now have a more consistent approach to integrating new vendors and handling real-time data, and the architecture is easier for our team to maintain and extend. It has given us a solid foundation to build further on.

Kristof Phillips CTO, Gridual

Contact

Have a question or want to explore how we can support your next project? Reach out and we’ll get back to you quickly.

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Tell us about your data or AI challenge. We read every message and reply personally.

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