How egglife Turned Data It Couldn't Share Into a Complete Picture of Its Retail Performance, Without Exposing a Single Retailer's Numbers to AI

At a glance

• Company: egglife foods
• Industry: Consumer packaged goods, egg-based foods sold through national and regional retailers
• Teams using Nera: Sales and category analytics
• The challenge: Understanding performance across retail partners when the data that explains it is too sensitive to combine or hand to an AI tool
• What changed: Sensitive fields were encrypted before analysis, so retailer data could be combined and analyzed with AI without being exposed
• Headline result: Time to cross-retailer insight cut from weeks to days, across 6 retail partners

The problem

egglife is one of the fastest-growing brands in refrigerated foods. Its egg white wraps and other egg-based products are sold at retailers including Target and Costco, and the company has been named to the Inc. 5000 list of fastest-growing companies in America.

Growth like that brings harder questions. Which retail partners are driving it? Where are pricing and promotions paying off, and where are they not? How is egglife performing against the rest of the category in each channel?

The answers live in egglife's retail data: sell-through, unit sales, pricing and promotion results from each retail partner. That data is competitively sensitive, and much of it comes with agreements that limit how it can be used and who can see it. So it gets treated as confidential in its entirety, not just the fields that matter. Combining it meant weeks of review before any analysis began, and the most sensitive fields were often left out. Putting it into a public AI tool was never an option.

The result was a familiar one: plenty of data, and only a partial view of what it said.

Combining data without exposing it: from all-or-nothing to precise

Nera changed the question from "can this data be shared?" to "which parts of it are actually sensitive?"

Sensitive fields such as unit sales, pricing and performance measures were encrypted before they left egglife's environment. Non-sensitive metadata such as product attributes, category labels and store formats stayed visible, so data from different retail partners could still be aligned and checked for quality.

The analysis then ran directly on the encrypted data. The AI model received the questions, never the underlying numbers. Only the final aggregate results were decrypted, and only for egglife's authorized team.

That precision is what unlocked the full picture. Fields that had always been held back could be included, because including them no longer meant exposing them. egglife could compare performance across 6 retail partners in a single analysis for the first time, and ask follow-up questions in plain language instead of waiting on a new report for each one.

Contracting and onboarding: from case-by-case negotiation to a repeatable process

Before Nera, every new data source started with the same conversation: who gets access, what can they see, who is liable if something leaks. Each one took about 6 weeks of legal and contract review before analysis could start.

With raw values never exposed, most of that conversation went away. Adding a new retail partner's data became a repeatable setup rather than a fresh negotiation, and review time dropped to under a week. Time from new data to usable insight fell by about 75%, from weeks to days.

And because adding data or running more questions doesn't add exposure, egglife can expand its analysis without expanding its risk.

Where this goes next

With the foundation in place, egglife can take the same approach further: bringing in additional retail partners as distribution grows, measuring promotion and pricing performance across channels side by side, and supporting expansion into new markets with the same level of insight it has at home.

"The pattern here shows up everywhere sensitive data needs to be combined across organizations that don't fully trust each other," said Rami Akeela, Ph.D., Founder and CEO of Nera Systems. "egglife didn't need to choose between seeing the whole picture and protecting the data behind it. They got both."