Alteryx launches BigQuery invoice workflows for finance

Alteryx introduced Live Query for Google Cloud BigQuery to handle unstructured documents, such as invoices, within BigQuery.

The launch targets finance and operations workflows where teams process large volumes of PDF invoices with inconsistent formats and frequent data quality issues. Users can build browser-based workflows to extract fields from documents, standardise data, compare it with records already stored in BigQuery, and flag exceptions without writing code.

The setup keeps historical invoice data in BigQuery while staging invoice PDFs in Google Cloud Storage. Extraction can use either Gemini or Document AI, feeding the results back into the workflow for classification, validation, and routing.

The system uses SQL pushdown, so transformation and reconciliation logic runs directly in BigQuery rather than in separate preparation tools. This is intended to reduce data movement and keep processing aligned with existing governance and access controls in the warehouse.

Invoice workflow

The example starts with invoice PDFs stored in Google Cloud Storage. The workflow identifies files and related metadata, including file paths, size, and timestamps, before sending selected documents to extraction steps.

It then pulls structured and semi-structured fields from invoices, including invoice numbers, invoice amounts, vendor names, net amounts, tax amounts, and line items. During design, it processes only a small subset of documents for preview, while production runs across the full set of PDFs.

After extraction, the workflow classifies line-item text to label spending into predefined categories such as industrial or office supplies. According to Alteryx, this makes the information more useful for reconciliation, exception handling, and reporting.

The workflow then cleans and standardises invoice numbers so formatting differences do not mask duplicates. It joins current invoice records with historical data in BigQuery to identify existing invoice numbers and flags them as potential duplicates.

It then applies business rules to the remaining records to identify issues such as missing fields, invalid totals, and amount mismatches. One example rule checks whether an invoice amount matches the sum of the net amount and tax amount.

The process produces two outputs: one for invoices matched to historical records and marked as potential duplicates, and another for current invoices that have been validated and prepared for unpaid-invoice processing. The aim is to turn document processing into governed operational outputs that finance teams can review and act on.

Governance focus

The architecture splits the process into three layers. Workflow authoring happens in the browser, Alteryx One platform services coordinate and plan execution, and Google Cloud serves as the data and execution layer, where governed data remains resident.

According to Alteryx, the platform services manage request handling and result retrieval between the browser workflow and the customer data environment, while adding traceability and observability across execution. This is intended to support auditability in document-heavy business processes.

The emphasis on keeping processing within BigQuery reflects a broader push by cloud data platform providers and analytics vendors to limit unnecessary data transfers between tools. For companies dealing with invoices and other operational documents at scale, reducing movement between extraction software, preparation environments, and data warehouses can help avoid duplicate records and manual reconciliation.

Alteryx said the pattern can be applied beyond small batches of files and is intended for use across millions of unstructured documents. The company positioned the product as a way to combine AI-led extraction with warehouse-based reconciliation and controls already used by enterprise data teams.

"What stood out to me about Alteryx One: Google Edition was how naturally it fits into the Google Cloud experience. I'm excited about the opportunity to make analytics more accessible by enabling business users to work with BigQuery data through an intuitive, governed experience," said Michael Wyant, Vice President, Enterprise Data & Corporate Solutions, Papa Johns.