Data science you can ship on Monday.
From clean data warehouses to predictive models and experimentation — analytics that change what you do next week, not just what you describe in board decks.
Most analytics work is descriptive: here is what happened. We focus on the harder work — here is what to do about it. That requires the unglamorous foundations (a real warehouse, clean models, governed metrics) and then the interesting parts on top (forecasts, segmentation, causal analysis, experimentation).
Concrete deliverables, named owners, weekly cadence.
Data Warehouse Foundation
Modern data stack — ingestion, transformation, governance — built on dbt with BigQuery or Snowflake as the warehouse.
Metric Layer & Reporting
A single source of truth for the numbers everyone in the company quotes. With versioning, ownership, and tests.
Forecasting & Prediction
Revenue forecasts, demand prediction, churn models — built so model output flows into operational decisions, not slides.
Customer Segmentation
RFM, behavioral, and value-based segmentation pushed back to marketing platforms for actual activation.
Experimentation Platform
A/B testing infrastructure with the statistical rigor to avoid drawing wrong conclusions from noisy data.
Causal & Incrementality
Designs and analyses that quantify the actual lift of marketing spend, product changes, and operational decisions.
A repeatable process, sized to the question.
Inventory
We catalogue the data you have, the questions you wish you could answer, and the decisions you'd make differently.
Foundation
Warehouse, ingestion, transformation, and a metric layer in place. Slow but non-negotiable. Most engagements start here.
Models
Forecasts, predictions, and segmentations built on top of the foundation — each tied to a specific decision someone has to make.
Compound
Quarterly review of which analyses are being used and which aren't. We deprecate what nobody acts on.
Common questions before we get started.
We don't have a warehouse yet — is that a problem?
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No. About half our engagements start with setting one up. We can have a basic warehouse in production inside three weeks.
Do we need a full data team?
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Not at the start. We can run the function as a service for businesses under about $20M in revenue, and help you hire your first data person when the time is right.
Will the models work after you leave?
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Yes. Everything is in version control, with tests and documentation. We pair with your team during the engagement so operations are transferable.
Can you do AI/ML beyond classical analytics?
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Yes — recommendation systems, NLP, embedding-based search, and selective use of LLMs in production workflows where they earn their cost.
Most growth problems take more than one discipline.
Complex Web Applications
Production-grade SaaS, marketplaces, internal tools, and customer-facing platforms — built to be reliable today and easy to extend tomorrow.
ExploreService · FinanceFinancial Analysis
Unit economics, cohort retention, and operating models built for SaaS, D2C, and marketplace businesses — the numbers behind every decision worth making.
ExploreBring us your hardest data science problem.
Tell us about it in a 30-minute intro call. If we can't help, we'll point you to someone who can.
Book an intro call