AI tools will keep changing. What you keep is the context.
Context is what your numbers mean, and who stands behind them. We build it so any AI can work fast on your data, and your people stay accountable for every answer.
Most of our work is with multi-location operators and companies that have grown by acquisition, where the same number lives in several systems and means something different to each team.
Your context writes the score.
More data isn’t always the answer.
Usually the numbers are there. They’re just out of tune with what people mean by them.
Most organizations already have the data, the tools, and the people. What’s missing is agreement on what the numbers mean, and a place where that agreement lives. So analysts reconcile instead of analyze, and leaders hedge decisions because they can’t tell which version is real.
Reporting lives across disconnected systems.
Different teams define the same metric differently.
Excel becomes the bridge between systems.
The board asks for a number you can't produce cleanly.
Leadership isn't sure which version of the number to trust.
Nobody owns the definition, so nobody can change it.
Four things every number should carry.
Every number your business runs on has a meaning, an owner, a calculation, and limits. Usually those live in people’s heads. We get them agreed and build them into the data model, where every report and every AI tool reads them.
What it means.
One definition per metric, agreed and readable on one page.
Who stands behind it.
A named owner for every number, with the authority to change it.
How it's produced.
The logic from source system to final figure, traceable.
What it can't tell you.
Caveats and known gaps, stated where people read the number.
Tools get replaced. Dashboards get rebuilt. The context behind them carries into every report and every AI tool that comes next, so it’s the one part of the investment you never make twice.
One context. Three ways to use it.
Most of our work starts with the numbers leadership argues about most. We build the context around those first, and it carries into everything below.
Context-rich dashboard builds.
Dashboards that carry their own definitions, owners, caveats, and the next question.
Conversational analytics.
Plain-language questions, answered from a semantic model that already knows what your numbers mean.
AI readiness and adoption.
A roadmap for where AI fits first, and the change management that gets people using it.
Start with the decision. Build backward from there.
We don’t begin with a dashboard or a tool. We begin with the decision the business needs to make, then work back to the definitions, data, and people it depends on.
Discover.
Understand the decision, the people who make it, and the systems behind it.
Define.
Agree on what each metric means and who owns it. Write it down on one page.
Build.
Put that context into the data model, then build the reporting on top.
Validate.
Test the numbers against how the business actually runs, not only against the spec.
Enable.
Hand over ownership, document the model, and train the people who will use it, including with AI.
Scale.
Extend the same definitions to the next domain, report, or tool.
Every step leaves something written down. That’s what your team keeps when we leave.
Why it stays in tune.
The definitions come first.
Most analytics projects deliver a dashboard and leave the metric disputes in place. Our first deliverable is agreement on what things mean. For a multi-location automotive service company, that meant a channel taxonomy and a metric dictionary before the first report page.
Both halves of the problem.
Sarah builds the systems and the data. Cihan works on the people and decisions those systems serve. Analytics projects fail on the second half at least as often as the first.
Adoption has a line, an owner, and a date.
About 10–15% of each engagement is adoption: named artifacts, a named owner on your side, and the date it becomes theirs.
Making marketing channels comparable before making them visible.
A multi-location automotive service company spent across paid search, paid social, direct mail, and email. Each channel reported in its own system, on its own terms, so leadership couldn’t compare them or trace spend to appointments.
We started with definitions: a channel taxonomy, a UTM tagging framework, and attribution and matchback logic. Only then did we build.
A marketing data platform and funnel dashboard that follows activity from spend through calls and appointments to revenue, with a metric dictionary behind it.
Leadership can now follow marketing spend through calls and appointments to revenue, in one view.
AI can’t settle what your teams haven’t.
Leaders are deciding how fast to move on AI. Some roll it out everywhere at once; others start with one careful pilot. Either pace works if the data underneath is ready, and neither does if it isn’t. We help you build that foundation, and a roadmap for where AI fits first.
The AI assistants built into analytics tools answer from the data model underneath: its definitions, descriptions, relationships, and instructions. When those are thin or contradictory, so are the answers.
The work that makes a dashboard trustworthy is the same work that makes AI useful. Written into the model once, it holds whichever AI tool you use next.
Meet the founders
Sarah Çekcan
Nearly a decade leading BI and analytics governance inside a Fortune 500 manufacturer, followed by senior BI leadership at a major U.S. bank, where she leads a team of developers, engineers, and analysts. She is a Certified Change Management Professional (CCMP) and Prosci-certified, so adoption is part of how she builds, not an afterthought. At the manufacturer, she led the work to get teams to agree on one definition of an active dealer, and reports across functions finally matched.
Cihan Çekcan
More than ten years managing teams in education, and an MIT Product Management Certificate. Cihan works on the people side of every engagement: who will use the numbers, who owns them, and how the work becomes part of how a team runs day to day.
Start with one number you’d like to trust.
Tell us which one, and what decision it’s holding up.