Dashboards your team trusts. Data your AI can use.

We clean up your data, build the dashboards your leadership actually opens, and set it up so anyone on your team can ask it questions in plain English.

Book a 30-minute call

Lotse /ˈloːt.sə/ LOHT-suh German for harbor pilot: the expert who guides ships safely through difficult water.

Raw data, many definitions

One answerSame number in every dashboard and AI tool

You've invested in data. Decisions still run on gut feel.

Most teams don't need more dashboards. They need the ones they have to be trusted and used, and a foundation AI can work with.

Dashboards nobody opens. They get built, viewed twice, and abandoned.
Numbers that disagree. Two reports show different revenue, so people ignore both.
Charts without a "so what." Leaders still need an analyst to explain what to do.
Reporting by hand. Someone loses days every month rebuilding the same deck.
AI that answers wrong. Natural-language tools give confident, incorrect answers because metrics aren't defined consistently.

What we build

Every dashboard we build sits on clean, defined metrics, so adding plain-English questions later takes no rework.

Dashboards and reporting

Executive dashboards, operational reporting, migrations off spreadsheets, and redesigns of dashboards nobody uses.

AI-ready analytics

A semantic layer that defines your metrics once, then natural-language querying your team can trust, one area at a time.

Data foundation

Pipelines and models that keep your data in your own warehouse, so you're never locked into one tool.

Ongoing support and advisory

A monthly partnership for new reports and upkeep, plus an experienced second opinion when it counts: evaluating new tools, weighing data decisions, and planning what to build as you grow.

Start with a Data & AI Readiness Assessment

A fixed-scope, fixed-price engagement of two to three weeks. By the end, your team is asking questions of one area of your data in plain English, and you know exactly what it would take to expand.

Discovery and audit

We map your data sources, pipelines, and warehouse models, trace how key metrics are calculated from report to report, and check which dashboards actually get used.
Readiness scorecard

We test data quality and freshness, find conflicting metric definitions, and score documentation and access against what AI analytics actually needs.
Working proof of concept

Plain-English Q&A over one well-defined area, inside the tools you already use, plus a prioritized roadmap.

Ask about the assessment

We work inside the stack you have, or help you improve it

Snowflake, Sigma, Power BI, Tableau, and the tools around them. If part of your stack is holding you back, we'll recommend what to change and why. Either way, your data stays in your warehouse and your metric logic stays portable.

Who you'll work with

I'm Emmett Burns. For more than five years I've worked on both sides of the data: building the pipelines and models underneath, and presenting what they show to the executives who have to make the call.

Marketing and advertising: At Amsive and Rain the Growth Agency, I built data pipelines and dashboards for 30+ clients across 10+ media channels, including Staples, Blue Cross Blue Shield, Humana, and 1-800-Contacts. My recommendations helped Staples cut cost per order 28% over 2024.
Healthcare: At Aledade, a value-based care company, I built a customer feedback model that used an LLM to classify interviews, surveys, and support tickets into a Tableau dashboard executives used to plan releases. One recommendation from it shipped and raised customer satisfaction 12%.
AI-ready data: I lead implementation at Velz, building semantic models that make marketing data AI-ready inside each customer's own warehouse.

Let's look at how your team uses data today.

A 30-minute call to understand your setup and whether we're a fit. You'll leave with at least one useful idea either way.