Four practice areas. Picked to match the work your stack actually needs.
Engagements are scoped tightly — most start with a 2-week discovery, then a fixed-fee build phase with weekly check-ins.
Data Strategy & Architecture
Where you are vs. where you need to be — assessed honestly. We help leaders pick the right data stack, scope the next investment, and design an architecture your team can build on for years.
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Assessment & roadmap
Audit your current stack, score the gaps, and sequence the work that moves the metric.
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Architecture design
Warehouse, lakehouse, or streaming — picked for your workload, not the trend cycle.
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Data governance
Ownership, access, quality, and lineage policies your team can actually follow.
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Build-vs-buy & team shape
Tooling, vendor, and hiring decisions — un-vendored advice, written down.
Data Engineering
Pipelines and warehouses that downstream teams can rely on without a Slack message. We bring opinions on data contracts, idempotency, and observability — and the patience to apply them consistently.
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Batch & streaming pipelines
Airflow, dbt, Dagster, Kafka, Flink — picked for the workload, not the résumé.
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Warehousing & modeling
Snowflake, BigQuery, Redshift, Postgres — modeled for the questions you actually ask.
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Data contracts
Schema, ownership, and SLAs between producer and consumer teams.
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Quality & observability
Tests, freshness checks, and alerting that catches a bad pipeline before the dashboard does.
Analytics & BI
Numbers that drive decisions instead of debate. We help define the metrics, model them properly, and surface them where the work happens.
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Metrics layer
One definition per metric. Versioned, tested, and queryable.
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Dashboards
Tightly scoped views in Looker, Metabase, or Superset — built to be read in 30 seconds.
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Experimentation
A/B framework, sample-size planning, and post-hoc analyses you can publish.
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Decision support
One-off deep dives that answer a single sharp question well.
Machine Learning
From a labeled dataset to a working prototype your team can run. We design models, build the evaluation harness to defend the numbers, and hand back a system the in-house team can extend.
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Predictive models
Classification, regression, ranking — with the evaluation harness to defend the numbers.
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NLP & embeddings
Search, classification, RAG, and entity matching on your domain corpus.
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Computer vision
Detection, OCR, image similarity, and on-device or batch inference paths.
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Recommenders
Hybrid retrieval-and-rerank systems tuned to the business KPI, not just NDCG.
Not sure which practice fits?
Send a short brief. We'll respond with the smallest engagement that moves the needle.