400+Sites Covered
Unified on a single platform with no gaps.
AI-Powered Procurement Mapping Engine
AI Transformation | Recipe Costing Accuracy
Client: Global Food Services Enterprise | India
Duration: Phase 1 (6 Weeks)
Fixing food cost accuracy across 400 sites in 8 weeks
Heizen built an AI-powered system that replaced weeks of manual recipe costing work with automated, nightly accuracy at enterprise scale.
See the Impacts
8 WeeksDelivery Time
Vs. 6+ months for typical enterprise scope.
NightlyCost Updates
Live SAP data reflected automatically.
ZeroManual Cycle
Weeks of reconciliation work eliminated.
Know the Problem
APL data in SAP updates continuously, but the enterprise's kitchen ERP had no way to reflect those changes. This caused silent, compounding drift in recipe costs, leading Finance to make decisions on numbers that no longer reflected reality.
What is the Solution
Heizen built an Autobot — AI agent engine that ingests live SAP data nightly, classifies mapping issues across queues, and routes ambiguous cases to a human review layer before updating ERP automatically.
Our Approach: The 3-Layer Solution
01
Data Infrastructure
Traced exactly where data breaks between SAP and the kitchen system.
02
Software System
Built the Autobot classification engine with four distinct processing queues.
03
AI Agents
Runs nightly, classifies issues, explains decisions, and loops in humans only when needed.
Four Reasons: Why Chose Heizen
01
Speed to Value
8-week end-to-end delivery from discovery to deployment for a scope that typically takes 6+ months in enterprise environments.
02
Deep Domain Expertise
Heizen embedded in India's culinary and procurement workflows before writing a single line of code.
03
AI-Native Execution
Agentic architecture with intelligent queue classification, substantiated human-readable reasoning, and a built-in review layer for edge cases.
04
Outcome-Based Model
Heizen's engagement is tied to demonstrated results, not hours logged or headcount deployed.
Impact Statement
MOG-APL mapping that once took weeks per cycle now completes in hours. Recipe costs reflect live SAP data nightly. Mapping errors are classified and routed for review. And the team has full visibility across every site in real time.
The UI Experience: Intuitive Operations Hub
The Autobot dashboard gives the team a live command centre showing mapped vs. unmapped MOG progress, Blue Watch retirement alerts, original vs. incremental workload, and days-to-target tracking, all in one unified view across all 400 sites.
AI-Native Supply Chain Transformation
We transformed ITC's multi-million dollar spice network
AI Transformation of Food Service Operations
We unified Superfine Kitchen's fragmented systems into one AI-ready platform
ERP Synchronization | AI Mapping
We built an AI-powered nightly accuracy engine across 400 sites
FAQs
Frequently asked questions
What operational problem did the food-service ERP synchronization project solve?+
The published case study describes recipe-costing drift between SAP data and a kitchen ERP across more than 400 sites. Changes in source data did not reliably appear in recipe costs, creating manual mapping and reconciliation work. Heizen built a nightly synchronization and mapping workflow to classify issues and make unresolved cases visible to reviewers.
How does the nightly recipe-costing workflow handle exceptions?+
The described system ingests SAP data, sorts mapping problems into four processing queues and routes ambiguous cases for human review before updating the downstream ERP. The case study calls this an Autobot engine. The important control is that uncertain mappings do not simply pass through without review; the exact queue rules are specific to that deployment.
Could the same approach work with another ERP and recipe-management system?+
The architecture is potentially reusable where one system owns ingredient or item data while another calculates operational costs. The integration needs source-to-target mapping, change detection, exception policies, reconciliation and a human review mechanism. It is not a plug-and-play guarantee: data models and costing rules vary by enterprise.
What should finance and IT teams verify before implementing recipe-cost synchronization?+
Identify the system of record for item prices and recipes, the update frequency required, mapping ownership, approval thresholds, expected site coverage and reconciliation controls. Agree on what constitutes a successful mapping, how failures are logged, and how historical cost changes can be audited.