Three offerings, built on one context.
Most of our work is with multi-location operators. Each offering below reads from the same context: what each number means, who owns it, the logic that produces it, and what it can’t tell you. It’s data governance without the bureaucracy: one page per metric, not a binder.
Context-rich dashboard builds
Dashboards in the tools your teams already use, carrying their own definitions, owners, caveats, and the next question. This is where most engagements start.
- Practical data governance: metric dictionary, named owners, change process
- Semantic models on the platform you already run
- Descriptions, synonyms, and AI instructions written into the model
- Funnel, channel, and attribution reporting
- Executive and board reporting leadership can stand behind
- Data quality checks before a number reaches a dashboard
- Drill paths to the next question on high-stakes metrics
- Reporting automation
- Your own git repo, handed over at the end
Conversational analytics
Your team asks questions in plain language, and the answers come from a model that already knows what your numbers mean. Built on the AI features of your existing platform, or a dedicated natural-language layer, chosen for your environment.
- A question inventory from the people who will ask
- Scope set to metrics whose definitions are settled
- Semantic model preparation for AI
- Answers tested against known figures
- A running list of questions the model can't answer yet, and the definitions that would fix them
AI readiness and adoption
Some teams are rolling AI out everywhere; others are starting with one pilot. Either way, we map which decisions and numbers are ready for AI, what needs to be settled first, and how your people will actually take it up.
- An AI readiness roadmap: which decisions and metrics are ready, and in what order
- Data and definition gaps ranked by what AI needs first
- Change management planning and stakeholder engagement, led by a CCMP- and Prosci-certified practitioner
- AI fluency training on your own data and definitions
- Named owners and a handoff date
A small first step, then the build.
Every engagement starts with analytics strategy: which decisions matter, and which numbers they depend on.
Analytics Readiness Assessment
A fixed-scope first step toward a single source of truth. We map where your reporting comes from, where definitions diverge, where data quality breaks down, and where the manual steps are. You get a findings document, a data strategy roadmap, and a one-page metric dictionary for your most contested metrics. Usually about five weeks. Fixed fee, credited toward a build if you continue.
Scoped Build
A defined deliverable at a fixed fee, usually 6–12 weeks. Adoption and repo handoff are included, not added on.
Longer programs
Definitions, build, and adoption across several quarters. Let’s talk about scope.
Adoption has a line in the plan.
About 10–15% of each engagement is adoption: named artifacts, a named owner on your side, and a date when it becomes theirs. AI fluency training lives here too: your team learns to work with AI on your own data and definitions, and to spot an answer that needs a second look. Not a generic prompting workshop.
Change management is a discipline here, not a slide. Sarah is a Certified Change Management Professional (CCMP) and Prosci-certified, and has led adoption inside a Fortune 500 manufacturer and a major U.S. bank.
of projects with excellent change management meet their objectives
with poor change management
Source: Prosci, the correlation between change management and project success.