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Zitrino: Leading Forward
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EngineeringData & Analytics Engineering

Pipelines that hold, numbers finance will sign.

The layer every AI programme quietly depends on. We build ingestion, storage, retrieval and reporting with lineage and tests in place, so the answer a system gives can be traced back to the row it came from.

Talk to our teamAll practices
48h
From raw corpus to a searchable, permissioned index
90%+
Pipeline test coverage before a dataset is trusted
1 source
One definition per metric, not one per team
Full
Lineage from dashboard back to the row it came from
Scope of practice

What the work actually involves.

Foundations

Pipelines and storage

Ingestion that survives schema drift and a storage layout chosen for how the data is actually queried.

Batch and streaming ingestion
Lakehouse and warehouse modelling
Change data capture from core systems
Cost-aware partitioning and retention
Retrieval

AI-ready data

Most retrieval failures are data problems in disguise. We fix the corpus first.

Chunking, embedding and vector store design
Permission-aware retrieval at query time
Corpus hygiene and duplicate resolution
Evaluation sets built from real questions
Trust

Quality and governance

A number nobody trusts is worse than no number. Tests, lineage and ownership make the difference.

Data contracts and quality tests in CI
Column-level lineage and impact analysis
Row and column access control
Catalogue with named data owners
Use

Analytics and decisions

The last mile: a semantic layer and reporting that answers the question a director actually asked.

Semantic layer and metric definitions
Self-serve reporting that stays consistent
Forecasting and scenario models
Embedded analytics inside your products
Why Zitrino

Principles we hold to on the hard engagements too.

Model the domain
We build to how the business works so the next question does not require a new pipeline.
Tests before trust
Every dataset ships with contracts and checks. Silent breakage is the failure mode that costs the most.
Permissions travel with the data
Access control is enforced at retrieval, so an assistant cannot surface what the person could not open themselves.
Your team runs it after
Standard tooling, documented models, no bespoke framework only we understand.
Technology

Tools we engineer with.

Opinionated about patterns, not vendors. The right architecture for a workload matters more than a preferred logo, and we say so when a cheaper option will do.

Warehouse
SnowflakeDatabricksBigQuerySynapsePostgres
Pipelines
dbtAirflowDagsterKafkaFivetran
Vector and search
pgvectorPineconeWeaviateOpenSearchAzure AI Search
Analytics
Power BILookerTableauMetabaseSuperset

Show us the data you are working with.

We will tell you what is ready to build on, what needs work first, and what is not worth keeping.

Talk to our team