AI & data

One version of the numbers, ready for reporting and AI.

Most companies have plenty of data and little agreement on what it says. We build pipelines and a warehouse with tested definitions, so finance, product and leadership read the same numbers, and your AI features have clean data to work with.

Data engineering & analytics
6–14 weeks

Start a project Talk to us

When to call us

Sound familiar?

01
Reports are assembled by hand in spreadsheets every month.
02
Different teams quote different numbers for the same metric.
03
You want AI features but your data is scattered across tools.

How Tulmira handles it

Our approach, step by step

1
Agree the metrics
We define the business metrics that matter with the people who use them, in writing.
2
Build the pipelines
Automated ingestion from your products and tools, with schema checks and data-quality tests.
3
Model the warehouse
Clean, documented models in version control, so every number traces back to its source.
4
Deliver insight
Dashboards and self-service reporting, plus data products that feed your AI features.

Our standards

Non-negotiable practices

Metric definitions version-controlled and reviewed like code
Data-quality tests on every pipeline run
Personal data classified, minimised and access-controlled
Lineage from every dashboard back to source
Incremental loads to keep cloud costs predictable
Alerts when data is late or wrong, before someone notices in a meeting

What you get

Automated ingestion pipelines
A documented, tested data warehouse
Dashboards and self-service reporting
A data catalogue and metric definitions

Tools we use for this

dbt
BigQuery
Snowflake
PostgreSQL
Airflow
Python

FAQ

Common questions

Anything else, ask us directly.

Usually the one closest to your existing cloud. BigQuery, Snowflake and PostgreSQL all work well at most sizes; we recommend based on cost and your team's skills.