Client Overview

Blaze Pizza is a leading fast-casual pizza brand operating across hundreds of restaurant locations.

In the QSR industry, food cost is one of the most significant and dynamic drivers of restaurant economics. Ingredient prices move, vendor costs change, recipes evolve, promotions shift product mix, and purchasing patterns vary across locations. For leadership teams, understanding these movements quickly is critical to protecting margins and making informed sourcing, menu, pricing, and operational decisions.

Blaze had substantial data across purchasing, recipes, restaurant operations, and promotional activity, but turning that data into insight remained highly manual and dependent on specialist knowledge. Significant time was spent reconciling inputs and investigating variances after they occurred, creating a largely reactive view of food cost performance. The opportunity was to create a more connected intelligence capability that could help the organization understand not only what food cost was, but why it was changing and where attention was required.

The Challenge

Blaze’s food cost process brought together a complex set of inputs across Finance and Supply Chain, including vendor purchasing data, restaurant mappings, product information, recipes, promotional activity, and business-specific rules.

Each cycle required significant analyst effort to reconcile large datasets, resolve store and product mismatches, correct pack-size issues, incorporate recipe and campaign changes, and apply exceptions before producing a reliable COGS view.

Much of the logic sat outside formal systems, distributed across spreadsheets, historical files, and individual expertise. This created key-person dependency and made the process difficult to scale.

The challenge extended beyond producing the number. Understanding why food cost moved often required additional manual investigation across supplier pricing, ingredient changes, recipes, purchasing behavior, promotions, and store-level differences.

Blaze needed a more scalable model for food cost management that could automate repeatable work, preserve business context, surface exceptions, and improve visibility into the drivers of restaurant economics.

Our Approach

Gravitas began by mapping the end-to-end food cost ecosystem across Finance and Supply Chain.

Rather than treating the engagement as a traditional reporting automation initiative, the team mapped how food cost was actually managed across the business, including vendor relationships, ingredient economics, commercial terms, pack-size complexities, recipe dependencies, purchasing patterns, and key exceptions.

The team also documented how Finance and Supply Chain investigated cost movements in practice, separating supplier-driven changes from recipe, purchasing, store-level, and broader ingredient-cost effects.

This became the blueprint for a Food Cost Intelligence Agent, designed around four core capabilities:

1. Connected Food Cost Data Foundation

Gravitas brought together the datasets required to understand food cost across the organization, including purchasing activity, product and restaurant mappings, recipes, campaign changes, and other relevant operational inputs. Recurring reconciliation activities such as identifier matching, unit normalization, mapping validation, and data-quality checks were incorporated into the workflow.

The result was a more consistent data foundation capable of connecting cost movements across vendors, ingredients, menu items, and restaurants.

2. Embedded Business Logic and Institutional Knowledge

A critical part of the solution was capturing the knowledge that historically sat with individual analysts. Recurring business rules, known exceptions, product relationships, and decision logic were documented and incorporated into the agent.

Repeatable decisions could therefore be handled consistently, while ambiguous or material exceptions were surfaced for human review. This reduced key-person dependency without removing the financial oversight required for a business-critical process.

3. Automated Food Cost Calculation with Full Traceability

The agent automated the core workflow required to move from source purchasing data to restaurant-level food cost. Every output retained a clear lineage to the underlying source data, transformations, mappings, and business rules used to calculate it.

This enabled Finance to move significantly faster without sacrificing control, reducing a process that previously required hours of specialist effort to minutes while preserving the ability to investigate and validate results.

4. Pattern Detection and Cost Intelligence

The opportunity extended beyond automating the calculation itself.

With cost data and business logic connected, the agent created a foundation for proactively identifying cost drivers and anomalies across ingredients, vendors, recipes, campaigns, and restaurants. This helps teams distinguish supplier-driven changes from operational, purchasing, or menu-related effects and direct attention toward the areas with the greatest potential business impact.

Instead of beginning with a reported variance and manually assembling the data required to explain it, Finance and Supply Chain can increasingly move directly toward understanding what changed, what is driving it, and where action may be required.

Implementation and Validation

Because food cost directly affects financial reporting and profitability decisions, governance and validation were central to implementation. The agent was back-tested against historical reporting periods and compared with previously validated COGS outputs. Differences were investigated with Finance, allowing Gravitas to refine business rules, improve mappings, and capture exceptions that had historically depended on analyst judgment.

Human review remained embedded for material or ambiguous exceptions, allowing Blaze to automate repeatable activity without compromising control.

Key Outcomes

Within six weeks, Blaze established a governed Food Cost Intelligence Agent that materially improved the speed, transparency, and scalability of food cost management:

• ~10x faster processing: Hours of specialist effort were reduced to minutes within the governed workflow.

• Reduced FTE requirement: The agent replaced a highly manual, specialist-dependent process, reducing the ongoing analyst capacity required to operate it and enabling a leaner operating structure.

• 100% source traceability: Calculated outputs remained connected to their underlying source data and business logic.

• Reduced key-person dependency: Critical rules and recurring exception logic were institutionalized within the agent.

• Faster root-cause analysis: Connected cost, vendor, recipe, and restaurant data reduced the effort required to investigate food-cost movements.

• Scalable decision intelligence: The capability provides a foundation for deeper analysis across vendor economics, ingredient trends, menu profitability, campaign performance, and restaurant-level cost drivers.

Business Impact

For a multi-unit restaurant organization, improved food cost visibility extends beyond Finance efficiency. It can help the business respond more quickly to vendor changes, understand margin pressure, evaluate sourcing decisions, assess menu economics, and identify differences across the restaurant network.

By combining connected data, institutional knowledge, governed automation, and human oversight, Blaze created a more scalable approach to food cost management, giving Finance and Supply Chain a faster path from COGS calculation to understanding the drivers behind it.

The result is not simply a faster COGS process, but a more intelligent and scalable way to understand the economics behind food cost.

About Gravitas

Gravitas is a strategy and execution consulting firm specializing in QSR, retail, and consumer brands.

We help leadership teams accelerate growth, improve EBITDA, and execute transformation through a combination of senior advisory and proprietary platforms such as Growth Hero™ and Laminar™.

Our approach integrates strategy, execution, and real-time visibility, enabling organizations to deliver measurable outcomes in weeks rather than months.

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