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Transforming Order Management with AI Agents for a Leading B2B Bakery Supplier
Transforming Order Management with AI Agents for a Leading B2B Bakery Supplier



A leading B2B bakery supplier in Europe, renowned for delivering premium frozen baked goods to over 40 countries, was experiencing significant operational drag due to manual processing of inbound email orders. With daily volumes scaling rapidly and increasing order complexity, the company partnered with Beam AI to usher in a new era of agentic automation.
The Challenge
Before Beam AI's intervention, the company faced three critical workflow pain points:
High Manual Load: Order details had to be manually extracted from emails (including attachments) and re-entered into Microsoft Dynamics NAV (Navision), increasing the risk of delays and human errors.
Slow Turnaround: The end-to-end workflow, from receiving an order to generating transport and delivery notices, often took several hours, limiting throughput.
Fragmented Systems: Data formatting mismatches and email-based communication silos prevented smooth integration with their existing ERP and logistics systems.
The customer needed a solution that could operate reliably, handle various document formats, and drive speed without compromising accuracy.
Beam AI Solution: A Full-Stack Agentic Automation
Beam AI deployed a suite of intelligent agents using its AgentOS framework. Each agent was designed to handle a discrete step in the end-to-end order processing journey:
Workflow Automation
From initial email parsing to uploading structured data into Navision, the entire process is automated via a coordinated agent flow:
Trigger: Starts when a new email hits a monitored inbox.
Classification: Email is analyzed to check if it’s order-related.
Categorization: Outlook category updated (e.g., “Order – Processed”).
Rule Check: Applies customer-specific logic based on sender info.
Header Extraction: Pulls key order details (order #, dates, address, etc.).
Customer ID Match: Uses fuzzy logic to find correct ID from external DB.
Line Item Extraction: Parses product-level info (article #, quantity, etc.).
CSV Creation: Generates a standardized file for ERP import.
ERP Upload: Sends CSV via SFTP to Microsoft Navision.
Archival: Stores attachments with metadata in DocuWare.
Key Capabilities Leveraged
AgentOS Component | Role in Workflow |
---|---|
Graph | Defined the end-to-end logic for order classification, extraction, and upload. |
Tools | Powered email parsing, CSV creation, Navision, SFTP and Docuware upload |
Integrations | Connected to Outlook, NAV (ERP), and DocuWare for seamless data flow. |
Memory | Tracked order context, reducing reprocessing of duplicate emails. |
Triggers | Automated activation from email inbox monitoring. |
Use-Case Highlights
Each order triggers a cascading automation sequence. Below is a summary of how each step is handled agentically:
Stage | Action | Integration Type | Agent Capability |
---|---|---|---|
Trigger | Detect new order email | Event Trigger | Email Watcher |
Classify | Identify if the email contains an actionable order | Prompt + Tag | LLM-based Classifier |
Validate Customer | Check if sender follows special customer-specific rules | Rule Branching | Customer Rules Engine |
Categorize Email | Update Outlook category based on classification | Update | Outlook Category Updater |
Extract Header | Parse order head details from PDF/text | Prompt | Order Head Extractor (LLM) |
Enrich Customer ID | Lookup customer ID from external DB using address | Lookup | Fuzzy Matcher + ID Resolver |
Extract Products | Extract product line items from attachments | Prompt | Product Details Extractor (LLM) |
Generate CSV | Convert structured data into ERP-compatible | File Writer | CSV Generator |
Upload to ERP | Transfer | Integration | ERP Connector (SFTP) |
Archive Document | Upload source PDF to DocuWare | Integration | DMS Connector (DocuWare) |
Impact & Results
The automation yielded measurable and sustainable improvements:
Metric | Before Beam | After Beam |
---|---|---|
Email Handling Time | ~4 hours | <15 minutes |
Manual Data Errors | ~12% | <1% |
Order Processing Load | 100% manual | 85% automated |
Order Acknowledgement Speed | 6–12 hours | <1 hour |
ERP Sync Errors | Frequent | Rare |
⚡ Result: 85%+ of all order workflows are now fully automated, enabling the client to process hundreds of daily orders with minimal human intervention.
Client Quote
“What used to take a full workday is now handled in the background before lunch. We no longer worry about manual backlog, Beam’s automation just runs.”
Looking Ahead
Following the successful automation of order processing, Beam AI is now working with the client to roll out:
Production Planning Recommendations: Based on historical orders and AI-powered forecasting.
Invoice Automation: Generating and syncing customer invoices directly from ERP events.
End-to-End Transport Planning: Including dynamic scheduling with logistics providers.
This is just the beginning of a broader digital transformation roadmap aimed at optimizing all customer-facing operations through intelligent agents.
Conclusion
This case study illustrates the power of AgentOS-based automation in tackling high-volume, repetitive B2B workflows. With AI agents orchestrating tasks across communication, ERP systems, and document management, Beam AI enabled an order management lifecycle, setting a new benchmark for operational efficiency in the frozen goods distribution industry.
A leading B2B bakery supplier in Europe, renowned for delivering premium frozen baked goods to over 40 countries, was experiencing significant operational drag due to manual processing of inbound email orders. With daily volumes scaling rapidly and increasing order complexity, the company partnered with Beam AI to usher in a new era of agentic automation.
The Challenge
Before Beam AI's intervention, the company faced three critical workflow pain points:
High Manual Load: Order details had to be manually extracted from emails (including attachments) and re-entered into Microsoft Dynamics NAV (Navision), increasing the risk of delays and human errors.
Slow Turnaround: The end-to-end workflow, from receiving an order to generating transport and delivery notices, often took several hours, limiting throughput.
Fragmented Systems: Data formatting mismatches and email-based communication silos prevented smooth integration with their existing ERP and logistics systems.
The customer needed a solution that could operate reliably, handle various document formats, and drive speed without compromising accuracy.
Beam AI Solution: A Full-Stack Agentic Automation
Beam AI deployed a suite of intelligent agents using its AgentOS framework. Each agent was designed to handle a discrete step in the end-to-end order processing journey:
Workflow Automation
From initial email parsing to uploading structured data into Navision, the entire process is automated via a coordinated agent flow:
Trigger: Starts when a new email hits a monitored inbox.
Classification: Email is analyzed to check if it’s order-related.
Categorization: Outlook category updated (e.g., “Order – Processed”).
Rule Check: Applies customer-specific logic based on sender info.
Header Extraction: Pulls key order details (order #, dates, address, etc.).
Customer ID Match: Uses fuzzy logic to find correct ID from external DB.
Line Item Extraction: Parses product-level info (article #, quantity, etc.).
CSV Creation: Generates a standardized file for ERP import.
ERP Upload: Sends CSV via SFTP to Microsoft Navision.
Archival: Stores attachments with metadata in DocuWare.
Key Capabilities Leveraged
AgentOS Component | Role in Workflow |
---|---|
Graph | Defined the end-to-end logic for order classification, extraction, and upload. |
Tools | Powered email parsing, CSV creation, Navision, SFTP and Docuware upload |
Integrations | Connected to Outlook, NAV (ERP), and DocuWare for seamless data flow. |
Memory | Tracked order context, reducing reprocessing of duplicate emails. |
Triggers | Automated activation from email inbox monitoring. |
Use-Case Highlights
Each order triggers a cascading automation sequence. Below is a summary of how each step is handled agentically:
Stage | Action | Integration Type | Agent Capability |
---|---|---|---|
Trigger | Detect new order email | Event Trigger | Email Watcher |
Classify | Identify if the email contains an actionable order | Prompt + Tag | LLM-based Classifier |
Validate Customer | Check if sender follows special customer-specific rules | Rule Branching | Customer Rules Engine |
Categorize Email | Update Outlook category based on classification | Update | Outlook Category Updater |
Extract Header | Parse order head details from PDF/text | Prompt | Order Head Extractor (LLM) |
Enrich Customer ID | Lookup customer ID from external DB using address | Lookup | Fuzzy Matcher + ID Resolver |
Extract Products | Extract product line items from attachments | Prompt | Product Details Extractor (LLM) |
Generate CSV | Convert structured data into ERP-compatible | File Writer | CSV Generator |
Upload to ERP | Transfer | Integration | ERP Connector (SFTP) |
Archive Document | Upload source PDF to DocuWare | Integration | DMS Connector (DocuWare) |
Impact & Results
The automation yielded measurable and sustainable improvements:
Metric | Before Beam | After Beam |
---|---|---|
Email Handling Time | ~4 hours | <15 minutes |
Manual Data Errors | ~12% | <1% |
Order Processing Load | 100% manual | 85% automated |
Order Acknowledgement Speed | 6–12 hours | <1 hour |
ERP Sync Errors | Frequent | Rare |
⚡ Result: 85%+ of all order workflows are now fully automated, enabling the client to process hundreds of daily orders with minimal human intervention.
Client Quote
“What used to take a full workday is now handled in the background before lunch. We no longer worry about manual backlog, Beam’s automation just runs.”
Looking Ahead
Following the successful automation of order processing, Beam AI is now working with the client to roll out:
Production Planning Recommendations: Based on historical orders and AI-powered forecasting.
Invoice Automation: Generating and syncing customer invoices directly from ERP events.
End-to-End Transport Planning: Including dynamic scheduling with logistics providers.
This is just the beginning of a broader digital transformation roadmap aimed at optimizing all customer-facing operations through intelligent agents.
Conclusion
This case study illustrates the power of AgentOS-based automation in tackling high-volume, repetitive B2B workflows. With AI agents orchestrating tasks across communication, ERP systems, and document management, Beam AI enabled an order management lifecycle, setting a new benchmark for operational efficiency in the frozen goods distribution industry.
A leading B2B bakery supplier in Europe, renowned for delivering premium frozen baked goods to over 40 countries, was experiencing significant operational drag due to manual processing of inbound email orders. With daily volumes scaling rapidly and increasing order complexity, the company partnered with Beam AI to usher in a new era of agentic automation.
The Challenge
Before Beam AI's intervention, the company faced three critical workflow pain points:
High Manual Load: Order details had to be manually extracted from emails (including attachments) and re-entered into Microsoft Dynamics NAV (Navision), increasing the risk of delays and human errors.
Slow Turnaround: The end-to-end workflow, from receiving an order to generating transport and delivery notices, often took several hours, limiting throughput.
Fragmented Systems: Data formatting mismatches and email-based communication silos prevented smooth integration with their existing ERP and logistics systems.
The customer needed a solution that could operate reliably, handle various document formats, and drive speed without compromising accuracy.
Beam AI Solution: A Full-Stack Agentic Automation
Beam AI deployed a suite of intelligent agents using its AgentOS framework. Each agent was designed to handle a discrete step in the end-to-end order processing journey:
Workflow Automation
From initial email parsing to uploading structured data into Navision, the entire process is automated via a coordinated agent flow:
Trigger: Starts when a new email hits a monitored inbox.
Classification: Email is analyzed to check if it’s order-related.
Categorization: Outlook category updated (e.g., “Order – Processed”).
Rule Check: Applies customer-specific logic based on sender info.
Header Extraction: Pulls key order details (order #, dates, address, etc.).
Customer ID Match: Uses fuzzy logic to find correct ID from external DB.
Line Item Extraction: Parses product-level info (article #, quantity, etc.).
CSV Creation: Generates a standardized file for ERP import.
ERP Upload: Sends CSV via SFTP to Microsoft Navision.
Archival: Stores attachments with metadata in DocuWare.
Key Capabilities Leveraged
AgentOS Component | Role in Workflow |
---|---|
Graph | Defined the end-to-end logic for order classification, extraction, and upload. |
Tools | Powered email parsing, CSV creation, Navision, SFTP and Docuware upload |
Integrations | Connected to Outlook, NAV (ERP), and DocuWare for seamless data flow. |
Memory | Tracked order context, reducing reprocessing of duplicate emails. |
Triggers | Automated activation from email inbox monitoring. |
Use-Case Highlights
Each order triggers a cascading automation sequence. Below is a summary of how each step is handled agentically:
Stage | Action | Integration Type | Agent Capability |
---|---|---|---|
Trigger | Detect new order email | Event Trigger | Email Watcher |
Classify | Identify if the email contains an actionable order | Prompt + Tag | LLM-based Classifier |
Validate Customer | Check if sender follows special customer-specific rules | Rule Branching | Customer Rules Engine |
Categorize Email | Update Outlook category based on classification | Update | Outlook Category Updater |
Extract Header | Parse order head details from PDF/text | Prompt | Order Head Extractor (LLM) |
Enrich Customer ID | Lookup customer ID from external DB using address | Lookup | Fuzzy Matcher + ID Resolver |
Extract Products | Extract product line items from attachments | Prompt | Product Details Extractor (LLM) |
Generate CSV | Convert structured data into ERP-compatible | File Writer | CSV Generator |
Upload to ERP | Transfer | Integration | ERP Connector (SFTP) |
Archive Document | Upload source PDF to DocuWare | Integration | DMS Connector (DocuWare) |
Impact & Results
The automation yielded measurable and sustainable improvements:
Metric | Before Beam | After Beam |
---|---|---|
Email Handling Time | ~4 hours | <15 minutes |
Manual Data Errors | ~12% | <1% |
Order Processing Load | 100% manual | 85% automated |
Order Acknowledgement Speed | 6–12 hours | <1 hour |
ERP Sync Errors | Frequent | Rare |
⚡ Result: 85%+ of all order workflows are now fully automated, enabling the client to process hundreds of daily orders with minimal human intervention.
Client Quote
“What used to take a full workday is now handled in the background before lunch. We no longer worry about manual backlog, Beam’s automation just runs.”
Looking Ahead
Following the successful automation of order processing, Beam AI is now working with the client to roll out:
Production Planning Recommendations: Based on historical orders and AI-powered forecasting.
Invoice Automation: Generating and syncing customer invoices directly from ERP events.
End-to-End Transport Planning: Including dynamic scheduling with logistics providers.
This is just the beginning of a broader digital transformation roadmap aimed at optimizing all customer-facing operations through intelligent agents.
Conclusion
This case study illustrates the power of AgentOS-based automation in tackling high-volume, repetitive B2B workflows. With AI agents orchestrating tasks across communication, ERP systems, and document management, Beam AI enabled an order management lifecycle, setting a new benchmark for operational efficiency in the frozen goods distribution industry.
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