Services

Data Engineering, Cloud Solutions and MLOps Services

This section maps the DS Stream service portfolio: data engineering, cloud data platforms, MLOps and AI engineering. Our services guide organizations from strategy through execution, with an emphasis on efficiency, scalability and measurable outcomes. Start from the family that matches your problem, then open the specific service page below.

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Key takeaways

Four service families: Machine Learning & MLOps, Data Engineering, AI Engineering and Data Science & Analytics.
End-to-end expertise: from strategic consulting to technical execution, delivered by one team.
Technology-agnostic approach, with solutions tailored to each client's goals, industry demands and technical needs.
Experience across FMCG, finance, retail, healthcare and e-commerce.
Contact us and we reply within 4 hours on working days (Mon - Fri, 9am - 5pm CET).

How do you choose the right service page?

Pick the family that matches the stage you are stuck at. Data Engineering fixes data that is not usable. AI Engineering builds generative AI products. MLOps and Machine Learning move models into reliable production. Data Science & Analytics turns the resulting data into decisions.

Start here

Raw data is fragmented or slow - start with Data Engineering, then narrow down to Data Lake Architecture, Data Warehouse Design, Data Pipeline Architecture or ETL Process Optimization.
Models work in a notebook but not in production - start with MLOps, then MLOps Implementation, Machine Learning Pipeline, ML Monitoring or Custom Machine Learning.
GenAI pilots do not reach users - start with AI Engineering, then Generative AI Development, Agentic AI Architecture, Custom Chatbot Development or LLMOps.
Governance and audit are the blocker - see Generative AI Governance, AI Model Governance and AI Data Quality.
Retail and FMCG decisions need analytics - see Retail Data Integration, Shelf & Space Optimization Analytics, AI-Driven Assortment Optimization and Data Science.

What do all DS Stream services have in common?

Every engagement combines end-to-end expertise, from strategic consulting to technical execution, with a technology-agnostic approach. Solutions are tailored to each client's unique goals, industry demands and technical needs, and built on experience across sectors from FMCG, finance and retail to healthcare and e-commerce.

DS Stream leverages the latest in AI, data science and engineering to solve business challenges, and helps clients stay ahead in a fast-evolving digital landscape. Proof points are published as case studies, including real-time intelligence for the longevity ecosystem and category management analytics for Lorenz Polska.

Service overview

Services in this section

Data Engineering

Lakehouse platforms, batch and streaming pipelines, governed data products and cost-controlled operations for retail and FMCG leaders.

AI Engineering

Production-grade generative AI at enterprise scale: LLM agents, RAG and multi-agent systems with governance, evaluation and observability built in.

MLOps

Take models from notebook to production with monitoring, governance and automation that keep machine learning reliable.

Data Science

Predictive models, business analytics and decision intelligence systems that turn enterprise data into decisions.

AI Factory

An operating model and platform that takes GenAI use cases from pilot to governed production at scale.

Data Lake Architecture

Zoned storage, ingestion pipelines, catalog and governance on Azure and AWS, delivered production-ready.

Data Warehouse Design

Cloud-native data warehouses and lakehouses powering enterprise analytics, reporting and AI.

Data Pipeline Architecture

Ingestion, transformation, orchestration and observability on AWS, GCP and Azure, run in production.

Real Time Data Processing

Data observability for streaming pipelines: freshness, volume, schema, lineage and latency SLOs across Kafka, Flink and Spark.

ETL Process Optimization

Re-architecture of batch and streaming pipelines to cut runtime and cloud cost and stop silent failures.

AI Data Quality

End-to-end test data management for enterprise AI: synthetic data generation, masked production subsets and quality gates.

Agentic AI Architecture

Agentic AI from prototype to production: multi-agent systems, orchestration, secure tool access and LLMOps pipelines.

LLMOps

Monitoring, tracing, evaluation and governance for LLM-based systems running in production.

Retail Data Integration

Retail data from stores, e-commerce, supply chain and customer touchpoints unified into governed data products.

Software Engineering

Custom data apps and internal AI tools engineered for scale and reliability behind your data and AI workflows.

Choosing and starting a service

Which service should a data platform team start with?

Start with Data Engineering if raw data is fragmented, slow or untrusted. From there, choose the specific scope: Data Lake Architecture for zoned storage and governance, Data Warehouse Design for analytics modelling, or ETL Process Optimization when pipelines are too slow or too expensive.

What is the difference between MLOps and AI Engineering here?

MLOps covers classic machine learning in production: deployment, monitoring, governance and automation. AI Engineering covers generative AI products such as LLM agents, RAG and multi-agent systems. For LLM-specific operations, see LLMOps; for model and prompt governance, see AI Model Governance.

Do you work with a specific cloud or technology stack?

DS Stream uses a technology-agnostic approach, with tailored solutions that align with each client's unique goals, industry demands and technical needs. Service pages name the platforms involved, for example AWS, GCP and Azure in Data Pipeline Architecture, or Kafka, Flink and Spark in Real Time Data Processing.

Which services fit retail and FMCG teams?

Retail and FMCG organizations usually start with Retail Data Integration to unify channel data, then add category decisions through Shelf & Space Optimization Analytics and AI-Driven Assortment Optimization. Industry context is on the Retail and FMCG pages.

How do we start and how fast do you reply?

Contact us through the form and we will get back to you within 4 hours on working days (Mon - Fri, 9am - 5pm CET). If you are not sure which page fits, describe the problem and the stage you are at, and we route it to the right team. You can also review our case studies first.

Explore the rest of the site

Tell us which service you are considering and what is blocking it today. We will get back to you within 4 hours on working days (Mon - Fri, 9am - 5pm CET).