Services
Four services. One root cause.
Each of these addresses the same underlying issue from a different angle: data that was never built to be relied on. Below is exactly what each one involves, what you keep at the end, and how to tell if it's the one you need.
01
Data engineering
Information lives in separate tools and only comes together when a person exports it by hand. Reports are late, and every refresh is someone's afternoon.
What you walk away with
Reporting data that arrives on its own, on a schedule, without a person in the loop.
Timeline: Typically 4 to 10 weeks depending on the number of source systems.
What's delivered
- Automated pipelines from your source systems into one central place
- Scheduling, monitoring, and alerting so failures surface immediately
- Tests that catch bad data before it reaches a report
- Documentation your team can run the system from
Signals you need this
- — Someone rebuilds the same report every week
- — Reports depend on one person's local spreadsheet
- — Nobody knows a pipeline broke until a number looks wrong
02
Analytics engineering
Two departments report two different numbers for the same thing, and both can defend theirs. The debate happens in the meeting instead of before it.
What you walk away with
One definition of revenue, customer, and every other metric that matters, reused everywhere.
Timeline: Typically 3 to 8 weeks, often alongside a foundation build.
What's delivered
- Agreed business definitions written down and version-controlled
- Clean dimensional data models built for clarity, not just today's dashboard
- A tested transformation layer every report reads from
- A metric reference your team can point new hires to
Signals you need this
- — Finance, sales, and marketing numbers never reconcile
- — Nobody can answer "which number is right?"
- — The same metric is calculated differently in three tools
03
Data strategy
A build is about to start, and the questions that decide whether it works have not been asked yet. Fixing that later is expensive.
What you walk away with
A clear, agreed plan for what to build and in what order, and confidence it's the right thing.
Timeline: Typically 2 to 4 weeks.
What's delivered
- Structured discovery sessions with the people who use the numbers
- A written map of current systems, owners, and reporting workflows
- The gaps, disagreements, and risks surfaced before any code
- A prioritized roadmap with recommended sequencing
Signals you need this
- — You're evaluating a new platform and unsure it addresses the real issue
- — Reporting ownership is unclear across teams
- — Previous data projects delivered something nobody uses
04
AI-ready data
AI tools answer confidently and incorrectly, because they inherited unclear definitions and disconnected systems.
What you walk away with
A data layer your AI tools can be grounded in, so answers can be traced and trusted.
Timeline: Typically 4 to 8 weeks, and it depends on foundation work being in place.
What's delivered
- Business context and definitions structured so models can use them
- Consolidated, documented source data with clear lineage
- Guardrails and validation on what the model is allowed to draw from
- An honest assessment of what is and isn't ready yet
Signals you need this
- — An AI assistant gives answers your team has to double-check
- — You're being asked for an AI roadmap and don't have the data for it
- — Nobody can trace where an AI answer came from
Not sure which one you need?
That's what the discovery conversation is for.