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Hissho: AI Franchise Operations

In a Covalent-led program, Ternary is building the application layer that turns Hissho's franchise-wide AI intelligence into daily, accountable execution in stores.

Industry
Food service & franchise
Engagement
Engineering partner · Covalent-led program
The Hissho SushiOps360 store dashboard: a low-inventory alert for sushi rice and avocado, total sushi sold, shrinkage against last month, an AI assistant prompt, and a weekly inventory outlook with days-of-cover and stockout risk.

How we approached the work, what we built, and why it matters.

Hissho Sushi supports franchised locations across the United States, where execution quality is shaped by thousands of small daily decisions in stores. In a modernization program led by Covalent Resource Group, Ternary serves as the engineering partner building the platform’s application layer.

The challenge

At franchise scale, the gap is rarely a lack of data — it's the last mile between available intelligence and daily execution. Franchisees and regional teams faced fragmented tools across many systems of record, which meant time lost searching for answers, reconciling signals by hand, and reacting to issues late.

Our approach

We followed a decision-to-action chain: identify the workflows where people act every day — plan, review, order, coach, communicate — then shape role-specific interfaces so franchisees, regional teams, and corporate users each see the right controls, bound to secure, auditable integration contracts.

What we built

A mobile-first, role-aware application layer that sits between users and Hissho's AI and data ecosystem: production planning with editable recommendations and reasoned overrides, sales dashboards, ordering workflows informed by production context, a chat-style information hub with source-aware answers, pre-visit briefings for regional coaching, and a communication hub connecting headquarters to the field — all under role-based access and audit trails.

The outcome

Intelligence becomes repeatable field behavior. Recommendations arrive inside workflows where users can review, override with a reason, and follow through — preserving accountability while raising consistency. The program continues its staged rollout, module by module.

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