services

Data Engineering

Data Analytics | Enrichment | Analytics | AI and ML

Turn your data into a reliable, scalable asset

Data is only as valuable as its accuracy, accessibility, and relevance. Many organisations struggle with fragmented systems, poor data quality, and infrastructure that can’t scale with business needs — let alone support modern AI initiatives.

At Zartis, we build data ecosystems that are secure, high-quality, and built for long-term evolution. Whether you’re modernising your pipelines, unifying multiple data sources, implementing governance, or preparing for ML and LLM workloads, we provide the engineering expertise to get you there.

Everything you need to master your data

Our teams combine deep technical knowledge with practical execution to help you move from scattered data to a clean, trustworthy, AI-ready platform.

Data platform design & architecture

Modern, cloud-first architectures that provide a clear, reliable foundation for analytics, BI, reporting, and ML workloads.

Data for AI & Machine Learning

Feature engineering, vectorisation, embeddings, and data preparation designed specifically for high-performance ML and LLM systems.

ETL/ELT pipeline development

Streamlined and automated pipelines to collect, transform, validate, and distribute data across your organisation.

Zartis data quality engineering services

Data Quality & Reliability Engineering

Data completeness, accuracy, resiliency, monitoring, and alerting — ensuring your teams always work with trustworthy inputs.

Data Integration

Connecting disparate systems, APIs, third-party tools, and legacy platforms into a unified data environment and enable successful AI ingestion.

Data Governance & Security

Policies, lineage, access control, and compliance frameworks that support safe and responsible data use.

Data engineering capabilities

We help you design modern data foundations that power better decisions, smarter products, and AI-driven innovation.

Data architecture

We design scalable data architectures — from lakes and lakehouses to real-time, batch, and streaming systems — ensuring your organisation has a strong, flexible foundation for analytics and AI.

Data for ML & AI

We prepare and structure your data for ML and LLM use cases, including embedding pipelines, vector stores for RAG, and feature stores that make model-ready data consistently available.

Data quality & observability

We implement validation frameworks, schema enforcement, and automated monitoring so your data remains accurate, reliable, and trustworthy across every pipeline and environment.

Optimisation & efficiency

We identify and resolve pipeline bottlenecks, reduce compute and storage costs, and improve data freshness and processing speed across your workflows.

Governance & security

We establish the policies, controls, lineage tracking, and compliance frameworks needed to keep your data secure, well-managed, and aligned with regulatory requirements.

Cloud & infrastructure

We build and optimise cloud data environments on AWS, Azure, or GCP, including infrastructure-as-code setups and CI/CD pipelines tailored for reliable, scalable data workflows.

Are you looking for Team Augmentation for data teams?

Add specialised data engineers, data analysts, data scientists, or data QA experts who integrate seamlessly with your team.

Case study

Improved reasoning for a multi-agent system utilising large datasets

Discover how we enabled the AI agents to accurately reason about the best staff and service to match to customer inquiries from a vast and diverse dataset of B2B services and staff providers.

What our clients think

Zartis has been a valuable partner to us in scaling our product development. They are easy to work with, and transparent. They have helped us with the skills we’ve needed; state-of-the-art .NET stack. Zartis developers have continuously shown expertise in architecture & good coding practices.

ESW

We started thinking, if we want to accelerate with bigger projects, let's bring in some of the AI expertise in Zartis. We're in a great position in Compliance and Risks because we have a lot of SMEs in our own business. It's a beautiful complementary relationship because we can bring in the high level AI expertise from Zartis to work with our SMEs.

Compliance & Risks

We recognised we didn't have the expertise to build a scalable infrastructure, so we reached out to Zartis, and they quickly brought a team of both architects and engineers to support us. That was the foundation of our partnership , and it delivered great results. We now have a fully scalable product which supports our customers from the U.S. to Japan.

Innowatts

Our process

Long-term support, optimisation, and continuous improvement to keep your data systems reliable as your business evolves.

Discovery & assessment

We analyse current data systems, pain points, quality issues, and readiness for analytics or AI use cases.

Architecture & roadmapping

A clear plan detailing the target architecture, data flows, governance model, and implementation steps.

Build & integrate

We design pipelines, schemas, workflows, and infrastructure — always aligned with business and operational needs.

Data quality & hardening

Testing, validation, observability, and governance to ensure trust in the data.

AI-enabling enhancements

Embedding generation, feature engineering, vector stores, and ML-ready pipelines added when appropriate, not forced where unnecessary.

Handover & continuous improvement

Ongoing support, optimisation, and team enablement so your internal teams can maintain and evolve the platform confidently.

Data engineering insights

Discover our research & whitepapers on data and AI

Whitepaper

An Analysis of the Zartis AI Application Development Experiment

This whitepaper presents the key learnings from an internal Zartis prototype aimed at building an end-to-end AI application for processing complex Mergers & Acquisitions data.

Whitepaper

AI Solutions: Moving From POC to Production

This “pilot to production gap” is where countless hours and investments disappear. Discover insights from a panel of industry leaders, who shared their learnings at the 2025 Zartis AI Summit.

FAQs: Data engineering

Do I need a complete data overhaul to start with AI?

Not necessarily. In many cases, small improvements to data quality, governance, or pipeline stability can unlock meaningful AI use cases. We help you identify the quickest and most impactful path.

Can you work with our existing infrastructure?

Yes. We regularly support clients with legacy systems, hybrid environments, and partially modernised stacks. We adapt to your current setup rather than forcing a specific toolset.

What industries do you support?

Data engineering applies everywhere. We work with SaaS companies, fintech, cleantech, healthtech, mobility, and enterprises with complex data challenges.

How do you ensure data quality?

We implement validation frameworks, enforce standards, monitor pipelines, and set up automated testing to catch issues early.

Do you handle ongoing maintenance as well?

Yes. We provide long-term support, optimisation, and continuous improvement to keep your data systems reliable as your business evolves.

Can you help with data strategy and governance too?

Absolutely. Data maturity depends on more than infrastructure — we help define policies, ownership, and processes that make your data trustworthy and usable.

Ready to build a data foundation you can trust?

Take the first step, we
will take care of the rest