D-01Data warehouse design
A modern warehouse with modelled, tested and documented data.
- +Source ingestion (ELT)
- +dbt models & tests
- +Shared metric definitions
AI & Automation
Data analytics and dashboard development
One trusted version of the numbers, and the models to see what’s next.
Transform raw data into actionable intelligence with modern data stacks, dashboards, and predictive analytics.
Revenue$1.28M+8.4%
Churn2.1%−0.3
Forecast Q4$4.1M±4%
ActualForecastdbt · BigQuery · Looker
- Category
- AI & Automation
- Deliverables
- 04 core
- Core stack
- dbt Labs · Google BigQuery · Looker
- Typical first phase
- Discovery
The brief
Modern Data Stack Solutions & Predictive Business Analytics
We turn scattered operational data into decisions. From warehouse design to BI dashboards and predictive models, we build the pipelines and interfaces that let your team see what's happening and act on it in real time.
Without it
- Every team has its own spreadsheet, and none of them agree.
- Reports take days to assemble and are stale when they arrive.
- Forecasts are gut feel dressed up in a chart.
With Caudify
- A modelled warehouse that becomes the single source of truth.
- Dashboards that refresh themselves and answer real questions.
- Predictive models tied to decisions, not just charts.
Deliverables
What our data analytics service delivers.
Every engagement is scoped around these four building blocks, combined, sequenced and sized to what you actually need.
D-02BI dashboard development
Dashboards designed around decisions, with drill downs people actually use.
- +KPI design workshops
- +Self serve exploration
- +Access controls
D-03Predictive modeling
Forecasting, churn, scoring and demand models validated against history.
- +Feature engineering
- +Backtesting & validation
- +Model monitoring
D-04Real time analytics pipelines
Streaming data for the metrics that can’t wait until tomorrow.
- +Event streaming
- +Near real time aggregates
- +Anomaly alerting
Engagement
How a data analytics engagement runs.
The indicative shape of a first engagement. Phases overlap on purpose, so we start building while discovery is still sharpening the plan.
- 01DiscoverySources, questions, metric definitions.
- 02FoundationIngestion, warehouse and dbt models.
- 03InsightDashboards and self serve analytics.
- 04PredictionModels, backtests and monitoring.
This is indicative only. Every engagement is scoped with you before it starts.
Fit
Is data analytics right for you?
- 01
Your teams argue about whose numbers are right.
- 02
Reporting is manual, slow and fragile.
- 03
You want forecasting that holds up to scrutiny.
Not sure it fits? A free assessment will tell you
# Service manifest service: Data & Analytics category: AI & Automation deliverables: • Data warehouse design • BI dashboard development • Predictive modeling • Real time analytics pipelines stack: • dbt Labs • Google BigQuery • Looker • Python Data Engineering • Predictive Analytics • ETL/ELT Pipelines phases: 4 next_step: free assessment
Questions
Data analytics questions, answered.
- Q1Do we need a data warehouse?
- If your data lives in more than a couple of systems and several teams report on it, then yes. A warehouse with modelled metrics is what makes numbers consistent. For smaller setups we’ll say so and keep it lighter.
- Q2Which BI tool do you recommend?
- It depends on your team and stack. We work with Looker, Metabase, Power BI and others, and choose based on who will use it, how they’ll explore and what you already pay for.
- Q3How do you make predictions trustworthy?
- Models are backtested on historical data, compared against simple baselines and monitored for drift after launch. If a model doesn’t beat the baseline, we don’t ship it.
Start with a free assessment.
Tell us what you're building, improving or automating. We'll come back with an honest view of where data analytics fits and the fastest path to production.