EchoStar's Boost Mobile uses the Databricks Platform to predict retail store performance and translate complex machine learning outputs into actionable insights. By building an AI agent with Agent Bricks, the team enables real estate stakeholders to evaluate locations, understand key drivers and make high-stakes decisions in seconds. The same self-service philosophy now extends to Boost's dealer network through Booster for dealers, a Genie-powered analytics copilot for third-party resellers. The result is faster growth, governed self-service analytics and more confident decisions across both internal and dealer-facing teams.
Bridging the gap between data science and business decisions
At EchoStar's Boost Mobile, retail expansion depends on a single critical question: how will a store perform before any investment is made? The team measures success through activations, the number of customers who initiate service at a retail location, making accurate predictions essential to growth.
To answer this question, the Data Science team built sophisticated machine learning models trained on thousands of store locations. These models incorporate demographics, competitive density, historical performance and market dynamics to predict expected activations for any address. While highly accurate, the models introduced a new challenge: accessibility.
"We had the math, but we needed actionable intelligence," said Abhishict Pamula, Data Scientist II at Boost Mobile, EchoStar. Stakeholders relied on data teams to interpret results, generate SHapley Additive exPlanations (SHAP) explanations and answer follow-up questions. Each request required manual analysis and often took days to deliver. "The gap wasn't the data. It was accessibility," Abhishict explained.
This dependency slowed decision-making in a high-stakes environment where selecting the wrong retail location could result in significant financial loss. EchoStar needed a way to put insights directly into the hands of business users without requiring deep technical expertise.
Building AI-powered translation layers across the business
To solve this challenge, EchoStar built an AI agent using Agent Bricks on the Databricks Platform. This created a translation layer between complex machine learning outputs and business decision-making.
The agent allows users to interact through a conversational interface, asking questions such as "Compare these two locations" or "What is driving this prediction?" Behind the scenes, the agent interprets intent, retrieves model outputs and translates feature importance into clear, business-friendly explanations.
"We control the behavior, so the agent never exposes technical jargon. It translates everything into key business factors," said Jack Stein, Data Scientist at Boost Mobile, EchoStar. This ensures that stakeholders receive insights they can act on immediately without needing to understand underlying modeling techniques.
Rather than relying on retrieval-augmented generation (RAG), EchoStar implemented deterministic SQL tools within the Agent Bricks Custom Agents. These tools query structured data directly from SQL warehouses, ensuring responses are grounded in factual outputs. This approach minimizes hallucination risk and delivers consistent, reliable results for high-stakes decisions.
The entire solution runs on a unified platform. Data pipelines, feature engineering, model inference and agent orchestration all operate within Databricks. Unity Catalog provides governance and secure access control, while MLflow Tracing delivers full observability into agent behavior. Databricks Apps provides a dynamic user interface that updates in real time, including interactive maps and visualizations directly tied to agent responses.
Extending self-service to the dealer network with Genie
EchoStar is applying the same self-service principles beyond its internal teams. Booster for dealers is a dealer analytics copilot for Boost's reseller and indirect retail network, built with Databricks Genie and AI/BI. The experience lets dealer-facing users ask natural language questions about business performance, including incremental orders, revenue lift, campaign ROI, experimentation results and store performance.
Booster for dealers is EchoStar's first production-focused Genie space built for third-party dealers and internal account executives, and it is embedded directly into the existing Booster external app, so dealer users get answers inside a tool they already use, rather than switching to a separate reporting system.
Accelerating retail growth with real-time, self-service insights
With the AI agent in place, EchoStar transformed how retail decisions are made. Real estate stakeholders can now input an address, receive a prediction within seconds and immediately explore the factors driving performance. This happens without relying on data teams.
This shift enabled 100% self-service access to insights, eliminating the need for manual analysis and reducing data request turnaround time from days to near zero. During peak periods, the Data Science team saves more than 15 hours per week, allowing them to focus on improving models and solving higher-value problems.
The impact extends directly to business outcomes. The solution supported EchoStar's largest retail growth campaign in five years, helping teams identify high-performing locations and avoid costly missteps. Instead of relying on static reports or lengthy presentations, stakeholders can now interrogate predictions in real time and make decisions with confidence.
"With Databricks, we collapsed data, modeling and AI into a single experience," said Abhishict. "Now we're not just making data-driven decisions. We're making strategic decisions at the speed of conversation."
By combining Agent Bricks with governed data and deterministic tooling, EchoStar has turned complex machine learning into actionable intelligence. This enables faster growth and smarter investments across its retail footprint.
Driving commission and store performance gains for dealers
The self-service model is delivering similar value on the dealer side of the business. Teams are using Booster for dealers to evaluate store effectiveness, understand sales data and identify new ways to boost commissions, replacing the static email reports and Tableau dashboards that previously required manual pulls and follow-up requests.
Because Booster for dealers is embedded in the existing Booster app, dealer-facing users and internal account executives get governed, trusted answers without needing to request custom reports or wait on a data team, extending the same speed and self-service model that reshaped EchoStar's internal real estate decisions to its broader reseller network.