Data pipelines that power
enterprise AI-governed,
scalable, production-ready.
From data pipelines to production AI - we build the data infrastructure that makes enterprise AI reliable, governed, and scalable. Every layer is designed to support AI workloads, not just reporting.
We don't just move data - we build the foundations that make enterprise AI trustworthy, traceable, and production-grade.
Scope of Practice
Data Pipeline Engineering
We design and deliver batch and streaming data pipelines that are reliable, observable, and built for AI consumption - with data quality gates and lineage tracking built in from the start.
- Batch and streaming ingestion pipeline design
- ELT and ETL architecture with dbt and Spark
- Data quality frameworks and contract enforcement
- Pipeline observability, lineage, and alerting
Analytics & BI Modernisation
We modernise legacy BI estates onto Snowflake and Databricks - with dbt transformation layers, semantic models, and self-serve analytics that reduce dependency on data teams.
- Snowflake and Databricks data platform delivery
- dbt transformation layer and semantic model design
- BI modernisation to Looker, Power BI, or Tableau
- Self-serve analytics enablement and data literacy programs
Vector DB & RAG Architecture
We architect the retrieval infrastructure that makes enterprise AI applications reliable - vector databases, embedding pipelines, and RAG systems grounded in your own governed knowledge.
- Vector database selection and deployment (Pinecone, pgvector, Weaviate)
- Embedding pipeline design for enterprise document corpora
- RAG architecture for LLM-powered applications
- Semantic search and hybrid retrieval system design
Model Integration & Evaluation
We integrate LLMs into your applications and data systems - handling model selection, prompt engineering, fine-tuning where warranted, and evaluation frameworks that measure real-world performance.
- LLM integration across OpenAI, Anthropic, Gemini, and Mistral
- Prompt engineering and chain architecture
- Model fine-tuning and adapter-based customisation
- Evaluation frameworks for accuracy, latency, and cost
Technology Ecosystem
Tools we engineer with
Why Zitrino
What sets our engineering apart
Principles we apply to every engagement - not just the ones that are easy.
AI-first data architecture
Every platform we build is designed for AI workloads - with vector-ready storage, embedding pipelines, and retrieval infrastructure from day one.
Streaming and batch unified
We design pipelines that handle both batch and real-time data within one coherent architecture - avoiding the fragmented data stacks that slow down AI development.
RAG and semantic search expertise
We architect vector databases and retrieval pipelines that make your enterprise knowledge AI-accessible - grounded, auditable, and production-ready.
Governance from layer zero
Data contracts, lineage tracking, and access controls are built into every layer - so your AI systems stay trustworthy, auditable, and enterprise-ready.
Build your AI foundation.
Pipelines live in weeks.
Tell us about your current data estate and AI ambitions. We'll identify the highest-leverage data engineering investments and show you a path to production-grade AI foundations.