Delivering the same Power BI report to many tenants doesn't have to mean maintaining many semantic models. When the reporting logic is identical and only the data location changes, one parameterized model can serve every tenant.
We learned this while building a reconciliation report for a multi-tenant data migration. Every tenant needed the same answer: did all the data arrive at the destination intact? Rather than cloning and editing a model for each tenant, we built one reusable model and moved the tenant-specific details into Power Query parameters. Here's how it works, and why it made onboarding a routine task instead of a development project.
The report had the same purpose for every tenant: compare source and destination data after migration and identify any discrepancies.
However, each tenant had its own schemas.
Without parameterization, onboarding a new tenant would require manually updating schema references throughout the model. As more tenants were added, this would create multiple copies of essentially the same model.
This also increases the risk of inconsistencies. A fix applied to one model may not be applied to another, and over time the reports can start behaving differently.
We wanted a process that was repeatable, consistent, and easy to maintain.
We added two parameters to the semantic model, and they became the only tenant-specific configuration:
The server and database connection stay the same for everyone. Only the schema values change.
|
Tenant |
SourceSchema |
DestinationSchema |
|---|---|---|
|
Customer A |
CustomerA_Source |
CustomerA_Destination |
|
Customer B |
CustomerB_Source |
CustomerB_Destination |
Every query in the model references these parameters instead of a hardcoded schema name. Tenant details become configuration, and the reporting logic stays untouched.
With the reusable model in place, adding a tenant follows the same checklist every time:
No queries are rebuilt and no report pages are redesigned. The administrator changes two values and validates the output.
One of the biggest advantages of this approach is consistency.
Every tenant can use the same semantic model design and reconciliation logic. Tenant-specific values are kept in parameters instead of being scattered throughout the queries.
This also makes the process easier to document.
The organization can define who is responsible for onboarding tenants, who can change parameters, how refreshes are performed, and what validation is required after each deployment.
At the same time, tenant data isolation still needs to be handled through the appropriate database and Power BI security and permission model. Parameters should be treated as configuration, not as the only security control.
A parameter-driven model pays off in practical ways:
Above all, adding a tenant becomes an operational task rather than another development project.
When several tenants need the same Power BI reporting experience, a separate model per tenant usually isn't necessary. If the database structure is consistent and the main difference is the schema, Power Query parameters make one semantic model reusable across all of them.
With SourceSchema and DestinationSchema as the only tenant-specific settings, the reconciliation logic stays standardized and onboarding stays repeatable. The result is one model, configurable tenant values, and reporting that scales with your tenant base.
Introduction Imagine managing hundreds of shipment requests every week. Emails keep arriving, spreadsheets grow larger every day, vendor quotes come from multiple sources, and teams spend hours manually updating records and tracking approvals. What starts as a simple shipping process can quickly become a complex operational challenge. For many logistics and supply chain teams, this is a daily reality. Manual processes often lead to delayed shipments, duplicate records, missed communications, and limited visibility into the overall shipping lifecycle. As businesses grow, these challenges become even more difficult to manage efficiently. But what if shipment requests could be automatically processed the moment they arrive? What if vendor quotes could be extracted, organized, and matched to shipments without manual effort? And what if approvals, tracking, and updates happened seamlessly through intelligent automation? This is where Artificial Intelligence (AI) and cloud-based automation are transforming modern logistics operations. In this blog, we’ll explore how two intelligent solutions: the Shipping Log Agent and the Shipping Quote Agent, work together to automate shipment creation, vendor quote management, approvals, and booking processes. Built on a modern Azure-based architecture, these AI-powered agents help organizations reduce manual work, improve accuracy, accelerate decision-making, and create a more efficient logistics operation from end to end. The Challenge Many organizations still rely on emails, spreadsheets, and manual workflows to manage shipment requests and vendor quotations. As shipment volumes increase, teams spend significant time reviewing documents, entering data, comparing quotes, coordinating approvals, and updating shipment records. These manual processes often result in: Duplicate shipment records Delayed approvals and bookings Data entry errors Limited visibility across shipment activities Increased operational workload Slower response times to customers and vendors As logistics operations grow, these inefficiencies become increasingly difficult to manage and can directly impact service quality, costs, and operational performance. The Solution: AI-Powered Automation The solution combines Artificial Intelligence, workflow automation, and cloud-native technologies to streamline the entire shipping lifecycle. Instead of manually reviewing documents, extracting shipment details, managing vendor quotes, and coordinating approvals, the system automates these activities using AI-powered agents. The solution consists of two specialized AI-powered agents that work together to automate critical logistics processes: Shipping Log Agent: The Shipping Log Agent automates the processing of shipment requests received through email. How It Works When a shipment request email arrives, the agent automatically: Monitors and identifies shipment request emails Reads PDF and Excel attachments Uses AI to understand and extract shipment information Converts unstructured documents into structured shipment records Applies intelligent matching rules to identify potential duplicates Creates new shipment records or updates existing ones Routes exceptions through an approval workflow when required Stores validated data securely in a centralized SQL database By eliminating manual document processing, the Shipping Log Agent significantly improves efficiency while maintaining data quality. Shipping Quote Agent: Once shipment requests are approved, the Shipping Quote Agent streamlines the collection and evaluation of vendor quotations. How It Works The Shipping Quote Agent automatically: Generates quote requests for approved shipments Sends requests to vendors Receives vendor responses through email Extracts pricing information from PDF quotations using AI Categorizes freight charges and surcharges Matches quotes with corresponding shipment requests Routes quotes for coordinator review and approval Updates shipment records after approval This automation eliminates the need for manual quote comparison and data entry, reducing delays and administrative effort. Key Enhancements and Innovations Migration from Excel-based storage to SQL Database AI-driven document processing Intelligent shipment matching Automated approval workflows Record versioning and locking Improved scalability and performance Complete audit trail Conclusion As logistics operations become more complex, organizations need smarter ways to manage shipment workflows. The Shipping Log Agent and Shipping Quote Agent demonstrate how AI-powered automation can streamline operations, improve accuracy, and support scalable business growth. By combining Azure services with intelligent document processing, organizations can build a more efficient and future-ready logistics ecosystem.
