Business areas
We turn scattered data into a trusted analytics foundation and AI applications with measurable value. From data platforms and forecasting to generative AI assistants, we take models into real production.
- 260+
- data and AI engineers
- 140+
- models in production
- 2.5 PB
- data under management
Most organisations hold plenty of data but rarely use it to make faster decisions. Zentrova's team of more than 260 data and AI engineers helps you lay a solid data foundation first, then build the analytics, forecasting and generative AI applications on top of it. More than 140 models built by our teams are currently running in client production environments. Our work spans banking, retail, manufacturing, logistics and healthcare, and we design every solution around the privacy rules that apply to your sector.
Core capabilities
- Modern data platforms: data warehouses, data lakehouses and real-time data pipelines.
- Business analytics with a shared metrics layer and role-based dashboards.
- Applied machine learning: demand forecasting, risk scoring, fraud detection and predictive maintenance.
- Generative AI: LLM and RAG-based assistants, summarisation, classification and document extraction.
- Computer vision and OCR for quality inspection, document processing and electronic identity checks.
- Data governance: data catalogues, data quality and personal data protection.
Technology & methods
- Spark, Kafka, dbt and Airflow for data processing and orchestration.
- Python, widely used deep learning libraries and an MLOps platform to train, deploy and monitor models.
- Open-source or commercial language models, deployed on premises where security requirements demand it.
- Automated evaluation suites and safety guardrails for every generative AI application.
- Responsible AI principles: explainability, bias testing and human oversight.
Our process
- Frame the problem — select use cases with clear value and data that is ready to use.
- Assess the data — review sources, quality and gaps before building any model.
- Prove the value — a six-to-eight-week trial with success metrics agreed up front with your business owners.
- Put it into production — embed the model in real business workflows, not just a demo.
- Monitor and retrain — track accuracy, data drift and inference cost over time.
We only recommend AI where it genuinely outperforms the way things are done today. Every engagement starts with a specific business metric and ends when that metric has measurably improved. Along the way we build your team's own capability, so that dashboards, pipelines and models remain useful long after the project team has moved on. Typical first results arrive within a single quarter.
