Services
Practical data science, scoped to the team you have
Data strategy & maturity
Map the current landscape, name the gaps that actually matter, and leave with a 90-day roadmap tied to business goals.
Problem
Mid-size companies often have reports in five places, no shared definitions, and a backlog of 'we should do something with data' that never becomes a plan.
Solution
We interview stakeholders, audit tools and processes, and return a short, ROI-ranked set of next steps — not a shelfware assessment.
Example use cases
- Decide whether to buy, build, or pause a dashboard programme
- Name the three metrics leadership will actually run the business on
- Sequence pipeline, BI, and modelling work so it does not collide
Typical tools
- Existing warehouses
- Spreadsheets
- BI tools
- Internal databases
Business intelligence
Power BI, Tableau, or Looker views for the KPIs that run the operating rhythm — designed so the rest of the team can keep them current.
Problem
Dashboards get built, then rot, because nobody owns refresh, definitions, or the next question.
Solution
KPI models, executive views, and automated refresh. The goal is a source of truth your team can maintain.
Example use cases
- Weekly operating reviews from one set of numbers
- Department scorecards with shared definitions
- Alerts when a KPI leaves a range you set
Typical tools
- Power BI
- Tableau
- Looker
- SQL warehouses
Forecasting & prediction
Demand, revenue, inventory, and capacity models grounded in your history — with scenarios finance and ops can run themselves.
Problem
Forecasts live in a spreadsheet owned by one person, or in a model nobody can explain.
Solution
Time-series and causal models, scenario planning, and handover documentation so the work survives the first quarter.
Example use cases
- Demand planning with seasonality
- Revenue scenarios for budget cycles
- Capacity and staffing outlooks
Typical tools
- ERP extracts
- CRM
- Python / SQL
- Spreadsheet handoff
Machine learning proofs
Scoped PoCs for churn, scoring, anomaly detection, and ranking. Built to a go / no-go bar, not a notebook that never leaves a laptop.
Problem
ML work often stops as a slide. There is no baseline, no monitoring, and no decision.
Solution
Feature pipelines, a model that has to beat a simple baseline, and basic monitoring so you can decide whether to keep it.
Example use cases
- Churn or propensity scores for a sales team
- Anomaly flags on operational metrics
- Ranking or matching problems with a clear success metric
Typical tools
- Python
- Your warehouse
- Batch scoring
- Simple APIs
Data engineering
Reliable ingestion and modeling so analytics stop breaking every Monday. Lakehouse and warehouse patterns sized for a mid-market budget.
Problem
Pipelines are a pile of scripts. A source change silently breaks Friday's report.
Solution
ETL / ELT, dbt and SQL modeling, and quality checks sized to the team you actually have.
Example use cases
- Nightly loads from ops systems into a warehouse
- dbt models for finance and operations
- Tests that fail loudly when a column goes missing
Typical tools
- dbt
- SQL warehouses
- Fivetran / native connectors
- Object storage
Customer & revenue analytics
Segmentation, lifetime value, retention cohorts, and campaign insight that marketing and sales can act on the same week.
Problem
Acquisition spend is tracked; retention and mix are not. Teams argue from different extracts.
Solution
Behavioral segments, churn and propensity views, and attribution support without pretending the data is cleaner than it is.
Example use cases
- Cohort retention for a subscription product
- Simple LTV to inform acquisition spend
- Campaign reporting that matches finance
Typical tools
- CRM
- Billing
- Product events
- Ad platforms