Predictive Analytics & Business Intelligence
From dashboards to decisions
BI that doesn't just show the past — it predicts the next quarter.

What we build, and why it works.
Most BI projects stall because the data isn't ready or the dashboards never answer the question. We build pragmatic analytics stacks: clean data warehouse, a tested semantic layer, dashboards your team actually uses, and predictive models layered on top — churn, LTV, upsell, demand, fraud.
What you get
- Data warehouse
- Dashboard suite
- Predictive models
- Reverse-ETL
Built around the features that actually matter.
Modern data stack
Warehouse, ELT, dbt models, semantic layer.
Self-serve dashboards
Metabase, Looker, Hex, or custom — versioned and governed.
Predictive models
Churn, LTV, conversion, fraud, demand — productionised and monitored.
Experimentation
A/B framework with statistical rigour.
Reverse ETL
Push insights back into CRM, ad platforms, and product.
Data quality
Tests, freshness checks, and incident workflows.
Business benefits
What this looks like on your scorecard.
- Decisions backed by trusted, fresh data
- Higher conversion from targeted predictions
- Lower churn via early-warning models
- Faster experimentation cycles
Common use cases
Where we've shipped this, or something close to it.
- B2B SaaS revenue analytics
- eCommerce growth & retention
- Marketplace dynamics
- Operations & supply chain analytics
Typical tech stack
Our delivery process for predictive analytics & business intelligence.
Assess
Sources, current dashboards, decision gaps.
Architect
Warehouse, models, governance.
Build
Pipelines, dashboards, predictive models.
Activate
Reverse-ETL, experiments, ongoing analytics.
Related services
More from our ai & automation services.

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AI Customer Support Bot
AI-powered support agents that handle Tier-1 tickets, draft Tier-2 replies, and escalate cleanly.
Let's scope your predictive analytics & business intelligence.
Share a few details about your goals and constraints — we'll respond within one business day with sharper questions and a recommendation.
