AI / GenAI Engineering
Data science and analytics
Pragmatic analytics and ML for business questions — not papers. Forecasting, classification, anomaly detection, and BI you can self-serve.
The problem
Sound familiar?
- 01The data team has a queue; business questions wait weeks for SQL.
- 02You have BI tools but no insights — dashboards everyone ignores.
- 03Ad-hoc analysis lives in unreviewed notebooks across laptops.
What we deliver
Concrete outputs.
ELT pipelines with dbt models and tested transformations
Self-serve BI layer (Metabase, Looker, or Hex)
ML pipelines for forecasting, classification, or anomaly detection
Experiment design and statistical review for product launches
Analyst enablement — patterns, snippets, code review
Data dictionary and lineage documentation
Methodology
How we run it.
Phase 1
Audit
Data inventory, top business questions, BI usage.
Phase 2
Build
ELT + dbt, BI layer, first ML pipelines.
Phase 3
Enable
Analyst training, dashboard handover, ongoing review.
Related capabilities
What pairs well with this.
- AI / GenAI Engineering
ML pipelines and MLOps
Model lifecycle done properly — versioned, evaluated, monitored, retrained on a schedule.
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LLM applications and RAG systems
Retrieval-augmented generation pipelines that ground LLMs in your data with citations, audit trails, and a private deployment option.
Read more - Product Engineering
Product analytics and growth instrumentation
Event taxonomy, dashboards, and growth experiments — done before launch, not after the post-mortem.
Read more
Get started
Ready to scope data science and analytics?
Book 30 minutes — we’ll tell you honestly whether the partnership model fits or whether an SOW is the better path.