I help teams turn messy or high-stakes data problems into tailored interactive apps for visualization, analysis, modeling, and interpretation, so the work remains usable after the first delivery.
The goal is not just to produce a result. It is to build a clearer workflow that combines statistical knowledge with AI-assisted production and validation, making decisions faster, more transparent, and easier to trust.
Most clients do not just need a PDF or a finished chart. They need a project-specific environment where data can be explored, analyzed, modeled, validated, and interpreted repeatedly.
Every engagement starts from the actual decision problem, then becomes a tailored interface for the right summaries, comparisons, models, and outputs.
Support exploratory work, descriptive summaries, and practical models inside the same tool instead of scattering the workflow across files and emails.
The deliverable should help clients interpret the output, not just display it. That includes summaries, plain-language framing, and built-in guidance around what matters.
AI can speed up production, but only when paired with statistical discipline. I use both together so workflows become faster while validation remains explicit.
Visualization, analysis, modeling, and interpretation should live together instead of being split across disconnected deliverables.
Statistical knowledge provides rigor, while AI improves production speed and validation coverage.
Clients should quickly understand whether the right answer is an analysis result, a reusable app, or a hybrid workflow.
The point is not to ship a static answer and stop there. The point is to create a usable environment where analysis and interpretation can continue with less friction.
I start from the actual use case, user, and decision pressure so the app reflects the real workflow rather than a generic template.
I combine cleaned data, visual summaries, modeling steps, and interpretation into a single structured interface.
The final deliverable is not just output. It is a more effective and more reviewable decision process supported by both statistics and AI.
These are starting points for engagement. The real value is matching the depth of the app, analysis, and interpretation layer to the project itself.
Best for a small project that needs a cleaned workflow and a clear first analytical view.
Best for a project that needs repeated exploration, summaries, and communication-ready outputs.
Best for projects where statistical modeling and AI-assisted production need to work together in a reusable system.
The demo app is meant to show the difference between delivering a one-time result and delivering a tool that supports repeated exploration, modeling, interpretation, and validation.
The app is built around a more realistic consulting scenario: the client needs a project-specific interface for understanding trends, comparing geography or segments, checking a model, and interpreting what the output means.
Send a sample file and a short note about the decision problem. I can help determine whether the right solution is a project-based app for visualization, modeling, and interpretation rather than another one-time report.