The Work

End-to-end data work

Becoming a company that runs on its own data is a climb, not a switch. We've done the work at every stage and layer and we can meet you wherever you are on the path.

Strategy & Advisory

Embedded in the work, not just a slide deck.

The strategies we develop are practical and state-of-the-art aware. You get our judgment, frameworks, industry knowledge and experience as part of every engagement. Usually paired with prototypes we are already building to prove out concepts. When you need honest outside-in expert perspective, we'll give it.

  • Architecture and stack assessment
  • Tech selection: build vs buy, open source vs vendor
  • Roadmaps and phasing: prototype, MVP, production
  • Build-vs-hire-vs-outsource decisions
  • Diagrams, concepts, decisions, tradeoffs
  • Real, meaningful artifacts that help thinking and alignment

Tools we use

Excalidraw · Markdown · PowerPoint · Mermaid · whatever moves the conversation forward

Data Pipelines & Orchestration

If it moves data, we've worked with it.

Building, fixing, replacing the systems that move data - from APIs, databases, files, queues, anywhere - to where you actually need it. Reliable, observable, recoverable, documented. No 3am alerts that nobody knows how to handle.

  • Custom ELT / ETL development (Python, SQL, dbt)
  • Workflow orchestration (Airflow, Prefect, Dagster)
  • Modern ingestion (dlt, Meltano, Fivetran, Airbyte)
  • Streaming and real-time (Kafka, Kinesis, EventBridge)
  • Migration off legacy or unmaintained pipelines
  • Observability, alerting, runbooks — the unsexy stuff that keeps things alive

Tools we use

dbt · Airflow · Prefect · Dagster · dlt · Meltano · Airbyte · Kafka · Lambdas · Python

Databases & Platforms

Backbone of your data operations. We treat it that way.

Designing, building, and migrating data warehouses and operational databases. We model for query patterns and team workflow, not theoretical purity. Star schemas where they belong. Lakehouse where it makes sense. Plain Postgres when it's the right answer.

  • Greenfield warehouse design and build
  • Schema design and dimensional modeling
  • Migration off legacy systems (Teradata, Oracle, on-prem to cloud)
  • Query performance and cost optimization
  • Operational database design (OLTP, transactional)
  • Lakehouse and medallion architectures when scale demands it

Tools we use

Snowflake · Databricks · BigQuery · Redshift · Postgres · Teradata · DuckDB · Lakehouse

Data Quality & Governance

Make the data trustworthy.

Building the trust layer between raw data and everything that reads from it. Tests, observability, lineage, master data, catalogs, contracts, ownership. When dashboards disagree and predictions go sideways, the issue almost always lives here, not in the model or the BI tool.

  • Quality tests and assertions (dbt tests, Great Expectations, Soda, custom)
  • Data observability: freshness, volume, schema drift, anomaly alerting
  • Lineage and impact analysis (OpenLineage, dbt docs, native lakehouse lineage)
  • Master data management: entity resolution, golden records, cross-system IDs
  • Data catalogs and discovery (OpenMetadata, DataHub, Atlan)
  • Data contracts and schema governance: what producers promise consumers
  • Ownership models and runbooks: who's on the hook when something breaks

Tools we use

dbt tests · Great Expectations · Soda · Monte Carlo · OpenLineage · OpenMetadata · DataHub · Atlan

BI, Reporting & Dashboards

Where data meets people.

Executive dashboards, operational reports, self-service analytics. We build on the BI platform you already own (or one we'll help you choose). We model the data right, design the visuals to actually answer the question, and set up the governance so your team can continue refining independently and safely.

  • Executive and operational dashboards
  • Self-service analytics setup and governance
  • Semantic layer / metrics layer design
  • Embedded analytics in existing products
  • Migration between BI platforms
  • Report automation and distribution

Tools we use

Power BI · Tableau · Superset · Metabase · Dash · Looker · Mode

Analytics & ML

Finding the signal. Building the model.

Predictive models, segmentation, custom metrics and statistical work. The kind of analysis that ends up in a board deck or running inside production code. Modern ML/AI alongside classic techniques when the use case calls for it.

  • Predictive models (regression, classification, time-series, recommendation, anomaly detection)
  • Custom analytics and ad-hoc statistical deep-dives
  • Predictive features embedded in production applications
  • Modern AI work: embeddings, vector stores, RAG, agentic flows, LLM evals
  • Executive analysis and reporting
  • Statistical rigor paired with pragmatism and business friendliness

Tools we use

Python · R · SQL · MLflow · scikit-learn · TensorFlow · PyTorch · Statsmodels · Prophet · LangChain · pgvector · OpenAI/Anthropic SDKs

Custom Data Apps

When a read-only dashboard isn't enough.

Internal tools, customer-facing data products, full-stack apps where the data is the point. Built to spec when off-the-shelf BI doesn't fit. We use this approach when the workflow needs custom interactions, the audience is end-users, or both. We've shipped React apps, Python services, mobile apps, the occasional C# Blazor, plus the modern AI-enabled surfaces (chat, copilots, agents in the loop).

  • Internal tools and admin apps
  • Customer-facing analytics features
  • Full-stack web apps with data at the core
  • AI-enabled surfaces like chat over data, copilots, agents in the loop
  • Embedded predictive features in production apps
  • Mobile and cross-platform when needed
  • Interfaces for data exploration and for acting on what you find

Tools we use

React · Next.js · Streamlit · FastAPI · Ionic · Blazor · XAF (C#) · PowerApps

On AI

We use AI to ship code, not to sell magic.

We don't sell AI as a product, a package, or a rebrand. When your foundation is ready and the use case is real, we ship it — embeddings, vector stores, RAG, agentic flows, LLM evals, the lot.

When it isn't ready, we'll tell you what to fix first. In our experience, the missing piece almost always lives further down the stack: data quality, governance, contracts, or a use case that hasn't been stress-tested. Most AI projects fail there, not at the model.

Engagement Models

Most clients land on Flex.

A flexible monthly arrangement: capacity bracket, blended rate, easy to revise as priorities shift. Other models when they fit your use case — Retainer, Discovery, Fixed, Support, Embedded.

How we engage in full

Not sure where to start?

That's exactly why we're here. Tell us your situation; we'll tell you whether we're the right fit and where we'd start.

Get in touch