Collect and clean
Every source into one place on a schedule, with validation at the boundary and an alert when a feed stops arriving.
Data engineering & ML
Data engineering and machine learning on your real data.
Scattered spreadsheets, three systems that disagree, and a report somebody rebuilds by hand every Monday. We pipe it into one place, agree on what the numbers mean, and put a dashboard on top that answers questions.
What we build
The answer is in there. Nobody can reach it.
The common failure is not a missing model. It is that the same question gets three different answers depending on who exports which spreadsheet, so nobody trusts any of them and every decision goes back to instinct. Fixing that is unglamorous and it is worth more than any model you could put on top of it.
Pipelines that survive your messiest source
Real source systems export inconsistent CSVs, change a column without telling anyone, and go down on the day of the board meeting. We build ingestion that expects all three: schema checks at the boundary, retries and dead-letter handling, and an alert when a feed goes quiet rather than a dashboard that silently shows last week.
One warehouse, one definition
Before the modelling there is a conversation nobody enjoys: what counts as an active customer, when a sale is a sale, which timezone the day ends in. Those definitions get written into the transformation layer, versioned in Git, and tested. After that the number on the dashboard is the number in the board pack, because they are the same query.
Dashboards people open on Monday
A dashboard is used if it answers the question its reader actually has, loads quickly, and is honest about freshness. We build to the decision, not to the data — three charts somebody acts on beats a forty-tile wall that gets bookmarked once and never revisited.
Machine learning, where it earns its place
Forecasting, classification, scoring, anomaly detection, computer vision on images you already collect. We are straight about this: a well-built SQL rule beats a model for a great many problems, and we will tell you when that is the case. When a model genuinely is the right tool, it ships with the pipeline that feeds it, a measured baseline to beat, and monitoring for drift.
What you get
- Ingestion pipelines that survive your messiest source system
- One warehouse, with metric definitions everyone has agreed on
- Dashboards your team opens on Monday, not once a quarter
- Freshness and quality alerts, so stale data announces itself
- Models with a measured baseline, not just an accuracy number
- Transformations versioned in Git and tested like any other code
Stack we use
- PostgreSQL
- Python
- dbt
- Pandas
- Airflow
- Metabase
- scikit-learn
- OpenCV
How it runs
Raw data to a decision.
In that order. Skipping to the model is how data projects fail.
Model and define
The transformation layer where a metric gets exactly one definition, versioned and tested so two reports cannot disagree.
Serve and predict
Dashboards for the questions people ask, and where it genuinely pays, a model with a baseline and drift monitoring.
Selected work
Data work we have shipped.
Every one of these started as somebody’s bottleneck. See all eleven projects.
Questions
Data engineering & ML, answered.
We only have spreadsheets. Is that enough to start?
Usually yes, and it is where most projects begin. Spreadsheets tell us what your team already tracks and how they think about it, which is more useful than a clean schema somebody designed in the abstract. The first step is getting them into one place on a schedule so they stop being retyped.
Do we need machine learning, or just better reporting?
Most often, better reporting — and we will say so. Machine learning earns its place when the pattern is genuinely too complex to write down as a rule, and when you have enough history to learn from. Selling a model to a business that needs a warehouse is the most common waste in this field.
Where does the data live?
In your infrastructure, in your accounts, under your access control. We build on managed Postgres in your cloud by default. For anything regulated we keep the whole pipeline inside your perimeter and document every boundary it crosses.
How do you keep the dashboard from going stale?
Freshness is monitored, not hoped for. Every pipeline records when it last ran successfully, the dashboard shows it, and an alert fires when a feed goes quiet. A chart that silently displays last week is worse than a chart that is honestly empty.
Can you work with our existing BI tool?
Yes. Power BI, Looker Studio, Tableau, Metabase — the warehouse and the definitions are the valuable part, and they are tool-agnostic on purpose. If you have no tool yet we usually start with Metabase because it is quick to stand up and cheap to leave.
More of what we do
The other five disciplines.
Most projects need more than one. They are in the same team, so nothing waits on a handover. See all services.
Web & software development
Custom web applications, platforms and internal tools, from first wireframe to production.
ExploreAI agents & automation
Voice and chat agents, document pipelines and workflows that do the repetitive work.
ExploreCloud & DevOps
Infrastructure as code, CI/CD and monitoring, so deploys stop being an event.
ExploreAPIs & integrations
Documented, versioned APIs and connections to the systems your business already runs on.
ExploreMVP development
From idea to a product your users can try, in four to eight weeks, built to grow.
Explore