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Feastables Partners with Engine to Build an AI-Enabled BI Ecosystem in Just 4 Months
Challenge
Feastables, the fast-growing confectionery brand founded by YouTube icon Mr Beast, was growing rapidly but behind the scenes, data was scattered and hard to manage. Without a centralized data infrastructure, the team relied on manual spreadsheets to analyze fragmented data, limiting visibility and slowing decisions.
As they expanded into new retailers and SKUs they realized they needed to make a decision:
1. Spend 1 year+ hiring costly talent to build a modern data stack from scratch
2. Find an experienced partner who could stand up a scalable solution fast
Solution
Feastables chose Engine for their rare combination of data architecture, reporting and data science expertise backed by decades of experience working with CPGs and retailers.
In just 4 months, Engine stood up a centralized data lake and integrated data from disparate retailer sources, syndicated providers, shipments, and financial systems into the Engine Data Platform, giving Feastables a single source of truth.
This foundation unlocked enterprise wide visibility, enabling Feastables to quickly resolve sales gaps and out-of-stocks, leading to major space gains in key retailers. With Engine's embedded data science expertise, they adopted AI-powered tools like custom demand planning and A/B testing, capabilities that would have taken years to build in-house.
Results
A Scalable, Fully Operational Data Infrastructure in 4 Months
(3x Faster Than In-House)
Reduced Overstocks & Accurate Forecasting
Enterprise Visibility Surfaced Immediate Actionable Insight
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Download the full case study
Get the extended analysis with deeper insights including:
- Store-level findings across hundreds of locations
- Regional demand patterns driving stockouts
- Compliance challenges impacting performance

Findings:
📌 400 stores were actively adjusting inventory levels to reduce waste, ignoring higher mod allocations.
📌 300 stores were following recommended allocations but experiencing high markdown losses due to overstocking.

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