-->
Home / AI Solutions / Data Engineering
Pipelines that feed AI

The foundation behind production AI.

Ingestion, integration and governed pipelines that make your data AI-ready — connected to SAP, Salesforce, Siemens and the cloud.

What we do

Data Engineering on Mendix.

Production-grade data engineering delivered by a Mendix Platinum Partner, backed by Siemens Xcelerator.

Ingestion

ETL / ELT

Reliable batch and incremental pipelines into one model.

Lakehouse

Storage & modeling

Lakehouse patterns that scale with volume and cost.

Streaming

Real-time

Event-driven pipelines for low-latency use cases.

Quality

Trust & lineage

Validation, lineage and governance built into the flow.

Integration

Connectors

SAP, Salesforce, Siemens, AWS and REST/OData wired in.

ML data

Feature stores

Curated, reusable features for models and agents.

How we deliver

Discover. Build. Govern.

The same 21-day rhythm across every engagement — discovery in five days, a production proof in twenty-one.

Step 01

Discover

Pin the use case and the ROI hypothesis in a 5-day discovery.

Step 02

Build

A production PoC on Mendix in 21 days — real data, real users.

Step 03

Govern

Security, audit and model risk handled from day one.

Step 04

Scale

Grow on the same platform to 100K users — no rebuild.

Ready to put data engineering to work?

Tell us the outcome you're after. We'll bring the platform, the AI and the engineers who've done it before.

FAQ

Questions, answered.

What sources can you integrate?

SAP, Salesforce, Siemens platforms, cloud data stores, APIs and legacy systems via REST, OData, events and connectors.

How do you ensure data quality?

Validation, lineage and governance are built into the pipelines, not bolted on after.

Is this AI-ready data?

Yes — we curate features and structures designed for RAG, models and agents.

Do you support real-time?

Yes, with streaming and event-driven architectures where latency matters.

What is agentic AI?

AI systems where autonomous agents plan and take multi-step actions across tools and workflows — beyond a single chatbot reply.

What is retrieval-augmented generation (RAG)?

A technique that grounds an LLM in your own data at query time, improving accuracy and reducing hallucinations.

How is generative AI different from traditional AI?

Traditional AI predicts or classifies; generative AI creates content and powers copilots and agents.

How much does an AI proof of concept cost?

We deliver a fixed-fee, fixed-scope 21-day production PoC; the exact figure is set after a 5-day discovery.

Register

Select The Country Code