Why We
Started This.
Stratosphere began with the founders' shared love of technology and their interest in why people do what they do. They are technologists who love marketing, researchers who love a hard question, and operators who want technology to change what a business actually decides.
They kept seeing the same failure. Companies collected vast amounts of data and still could not answer the questions that mattered: which segment to pursue, who their most profitable customers were, whether to enter a new territory, whether to launch a product line. Infrastructure grew while confidence did not.
They chose Databricks for the implementation practice because they judged it the strongest foundation for turning large datasets into clarity, and for getting data, engineering, and marketing teams working from the same governed information.
The Same Senior People, Start To Finish
The same senior people who understand the question remain involved in the analysis, architecture, and implementation. Stratosphere does not sell a senior strategy team and hand delivery to a staffing pyramid.
Almost 20 years in technology, including AWS Professional Services. Clem brings software engineering, platform architecture, and production-delivery experience.
PhD in neuroscience, more than a decade of large-dataset analysis, and hands-on experience coding programs for studies and analysis. Chelsey brings study design, behavioral inquiry, and scientific discipline.
More than 20 years in marketing and firsthand experience of how the discipline has evolved. Abby brings commercial context, marketing strategy, and an instinct for using emerging technology to solve practical marketing problems.
What We Hold Ourselves To
Take responsibility for the result, not merely the assigned task. Anticipate what the outcome requires, raise risks early, and carry the work through handoff.
Make decisions around meaningful client value. Preserve transparency, recommend against poor-fit work, and don't optimize for billable activity at the client's expense.
Bias effort toward useful action and measurable improvement. Research and architecture should lead somewhere consequential.
Investigate, experiment, and strengthen expertise continuously. Treat assumptions as testable and distinguish evidence from opinion.
How The Values Show Up In Delivery
Define the decision before designing the system.
Artifacts matter because of what they enable.
Trustworthy insight requires sound data, method, governance, and implementation.
Senior practitioners remain accountable from assessment through delivery.
Ask better questions, test assumptions, and disclose uncertainty.