Data Analytics & Decision Intelligence
From scattered data to decisions you can defend.
Most companies have plenty of data and very little clarity. We make the numbers trustworthy, build the dashboards people actually open, and add forecasting where it changes a decision rather than just a chart.
Good fit when
Where this work pays off
- Leadership teams reconciling numbers across spreadsheets, ERPs and SaaS tools
- Operations that need forecasts for demand, inventory, cash or capacity
- Companies preparing their data for AI but unsure where to start
- Reports that take days to produce and are stale on arrival
What we build
What you get: numbers everyone trusts
- Data platform and pipelines
- A warehouse with reliable loading, transformation and quality checks, so the same number means the same thing everywhere.
- Decision dashboards
- Each metric defined once and reused, with views designed around the decisions each role has to make.
- Forecasting and prediction
- Demand, churn, risk and capacity models with plain accuracy reporting and a plan for keeping them current.
- Data ready for AI
- Cleaning, structuring and access control for the data your assistants and agents will depend on.
How it works
From an audit to trusted numbers
Audit
List the sources, the metrics and the decisions they are supposed to inform. Find the gaps and duplicates.
Define once
Agree each metric's definition once so every report tells the same story.
Build and automate
Pipelines, checks and dashboards delivered in slices, with your owners trained as we go.
Predict
Add forecasts and recommendations only where they change a decision.
A typical first engagement
What you need to bring
Discovery is a fixed-fee phase that ends in a written plan and a costed prototype scope. You can take that plan anywhere.
- Read access to the systems and spreadsheets the numbers come from
- The ten recurring decisions the business makes, and who makes them
- One owner who can settle what a metric means
Tools we typically reach for, chosen per project for fit and portability: PostgreSQL, BigQuery, Snowflake, dbt, Airflow / Dagster, Python, Metabase / Power BI / Looker, scikit-learn.
Frequently asked questions
Do we need a data warehouse before we can use AI?
Not always, but you need trustworthy, well-described data. We often start with the small, governed subset a first AI use case depends on and grow from there.
Which dashboard tool should we use?
Whichever your team will open. Metabase, Power BI and Looker are common choices; the metric definitions matter far more than the tool.
How accurate will forecasts be?
We report accuracy against a simple baseline on your own history before anything goes live, and we say so plainly when the data cannot support the precision you want.
Not sure which service fits?
Describe the problem in a few lines. We will tell you honestly whether it is an AI, data, automation or software problem, and what a sensible first step looks like.