Trusted metrics
Definitions everyone agrees on - conversion, retention, ops KPIs.
AI · Data Science & Analytics
Analytics and data science that drive decisions.
From clean pipelines to predictive models - insight your product and team can act on, not dashboards nobody opens.
Outcomes
What success looks like.
Predictive signals
Churn, demand, and risk scores when the data supports them.
Product-integrated insight
Surfaces in apps and admin tools, not only BI suites.
Capabilities
How we deliver in this area.
Data pipelines
Ingest, clean, and model events from apps and backends.
Exploration & reporting
Clear charts and narratives for founders and ops leads.
Predictive models
When history is rich enough to forecast usefully.
Experimentation support
Instrumentation for A/B and feature flags.
Feature stores (pragmatic)
Reusable signals for ML without over-engineering day one.
Privacy-aware analytics
Minimize PII and respect consent in your region.
How we run it
A clear path from brief to release.
Seniors stay close to the work. Status stays honest. The process bends to your stage.
01
Define decisions
What choice will this data change next week?
02
Inventory sources
Apps, DBs, sheets - and the gaps between them.
03
Deliver a thin insight loop
One pipeline and one surface that people actually use.
04
Expand models carefully
Add prediction only when baselines are solid.
Questions
Straight answers before you commit.
Do we need a data warehouse first?
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Do we need a data warehouse first?
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Not always. We start with the smallest reliable store that answers your question, then grow.
Can you work with our existing BI tool?
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Can you work with our existing BI tool?
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Yes - we can feed it, or build product-native views when operators live in your app.