Building an AI Document Extraction Workflow that Works
Artificial intelligence is becoming a practical part of back-office operations, particularly in document-heavy processes such as accounts payable. But building an AI document extraction workflow is not as simple as selecting a model, connecting it to invoices and waiting for clean data to appear.
Organizations need to make decisions about data quality, model performance, validation, cloud infrastructure and service-level agreements. The most effective projects begin with the business workflow rather than the technology. They define what data must be extracted, establish a reliable baseline for accuracy and determine how quickly results are needed.
That approach helps organizations avoid a common mistake: treating a high-performing AI model as a complete solution. In document processing, the model is only one component of a larger system that must produce accurate, auditable results at an acceptable cost.
Establish ground truth
The foundation of any document extraction project is ground-truth verified examples of the fields the system is expected to identify and return. For an accounts payable workflow, that might include vendor name, invoice number, invoice date, purchase order number, payment terms, total amount and line-item data.
Without ground truth, there is no dependable way to evaluate performance. A model may return an answer that looks plausible, but the organization cannot know whether it is correct, incomplete or based on the wrong information.
This becomes particularly important with generative AI. These systems can generate confident but inaccurate responses, known as hallucinations. In a document-processing workflow, an unsupported output might include a nonexistent value, a misidentified field or information copied from a previously processed document.
Those errors are more than minor quality issues. In finance, procurement, human resources and compliance workflows, inaccurate data can result in payment exceptions, reconciliation work, delayed approvals and diminished trust in automation.
A useful test set should reflect the documents that flow through the real business process. It should include clean, digitally generated files as well as poor scans, low-resolution images, skewed pages, multipage documents, tables, handwriting and different supplier formats. It should also include known problem documents that already require extra human attention.
The goal is not simply to achieve a single accuracy score. The goal is to understand where the system succeeds, where it fails and whether it can identify uncertainty before incorrect data moves downstream.
Test models against real documents
The AI market places significant attention on model size, parameter counts and benchmark results. Those measures can be useful, but they do not replace testing with an organization’s own documents. Parameters are the learned internal weights that help a model identify patterns and generate responses. A context window is different: It represents the amount of information a model can consider in a single interaction. Both can affect performance, but neither measure alone determines whether a model is appropriate for an invoice or document-extraction workflow.
In our efforts testing commercial and open-source models across platforms such as Anthropic, Azure and AWS Bedrock, the most capable models performed better than older or smaller alternatives for document extraction and contextual understanding. Using the same document sample set and a similar context window, one test produced 99% accuracy with Anthropic Sonnet 4.8, compared with 83% using Qwen 3.
That difference demonstrates why organizations should base model selection on documented testing rather than assumptions. Still, the highest-performing model is not always the best choice for production. Frontier models may offer stronger accuracy and reasoning capability, but their token costs can become difficult to justify at high document volumes. Smaller models may be less expensive, but they may not reliably handle complex invoices, line items or variable document layouts.
The best option often lies between those extremes. Organizations should evaluate each candidate on the factors that matter to their workflow:
• Field-level extraction accuracy
• Ability to handle poor-quality and nonstandard documents
• Performance on multipage invoices and line-item detail
• Cost per document at anticipated monthly volume
• Time required to process a document
• Ability to return structured data to downstream applications
• Exception handling and human-review requirements
• Security, data governance and auditability
The market is changing quickly. New models, larger context windows and lower-cost options are entering the market faster than many organizations can test and deploy them. That makes periodic evaluation necessary, but it also makes disciplined testing more important. A new model should earn a production role by performing well on the organization’s actual documents and operating requirements.
Design for validation, not blind trust
An AI model should not be the sole source of truth in a document workflow. Strong implementations provide a way to validate extracted data and route questionable results for review. Retrieval-augmented generation, or RAG, can support that objective. RAG connects a model to approved external data sources, enabling the model or workflow to retrieve relevant information before generating an output.
In document processing, those sources might include a vendor master file, purchase order system, contract repository, historical invoice database or other trusted enterprise records. The workflow can compare an extracted vendor name against approved supplier records, validate a purchase order number or flag a payment term that does not match the vendor agreement. This helps ground the AI’s output in verified business data rather than relying only on the contents of a document or the model’s training data.
RAG is not a substitute for data governance. Organizations must ensure that the connected data is current, properly secured and available only to authorized users. But it can be a valuable component in reducing unsupported outputs and improving auditability.
The most effective workflows also use confidence thresholds and exception queues. High-confidence results that pass validation checks can move through straight-through processing. Documents with missing information, low-confidence fields or mismatches against business records should be directed to a knowledgeable employee for review. This type of model does not eliminate human involvement. It focuses human attention on the documents where judgment is needed most.
Managed or Self-Hosted?
After selecting a model and designing a validation process, the next decision is where the model will run. For many organizations, that means choosing between a managed cloud service and self-hosted infrastructure. Managed services, including AWS Bedrock and Azure Content Understanding, reduce the burden of running the underlying environment. The provider manages much of the infrastructure, software maintenance and scaling, while the customer pays based on usage.
This approach can be attractive for teams that want to move quickly, lack machine-learning operations expertise or need to avoid managing GPU infrastructure. The tradeoff is variable cost. Usage-based charges can rise quickly when organizations process large document volumes, use image inputs or submit long, multipage files.
Self-hosted infrastructure has a different cost structure. Instead of paying a provider for model usage, the organization operates the model on its own cloud resources. This can eliminate managed-service token charges, but it shifts responsibility for GPUs, instances, containers, monitoring, security and scaling to the organization or its technology partner.
Based on an in-depth analysis of accounts payable use cases, our AWS development partner found that a self-hosted environment was about 30% less expensive than a managed service. That result should not be treated as universal. The economics will change based on processing volume, model choice, cloud pricing, infrastructure utilization and the required SLA. The central question is not whether self-hosting is always less expensive. It is whether the organization can operate enough infrastructure to meet demand without paying for idle capacity.
Let the SLA dictate the cost
Processing speed is a major driver of infrastructure costs. If documents must be processed immediately or in less than 10 minutes, the organization may need enough GPU capacity to accommodate peak demand at any time. Maintaining that capacity can make self-hosted infrastructure expensive, especially if demand fluctuates sharply during the day.
A more flexible SLA can improve the economics. If a document can take 10 minutes or more to process and the overall workflow allows processing to be completed within 24 hours, the organization may be able to batch workloads, manage queues and use infrastructure more efficiently.
That is why business-process analysis should precede technical architecture decisions. Stakeholders should identify the documents that genuinely require near-real-time processing and distinguish them from documents that can move through a scheduled or overnight workflow. Treating every document as urgent may create unnecessary costs.
GPU availability also belongs in the planning process. Demand for graphics processing units remains high, and availability can vary by provider and region. Organizations should assess available capacity, pricing and regional requirements before committing to a production architecture.
Use containers to scale
Organizations that choose self-hosted infrastructure typically need a way to add and remove compute capacity as workload demand changes. In AWS, one approach is to run models on Amazon EC2 instances in containers managed through Amazon Elastic Container Service, or Amazon ECS, with auto scaling enabled. This approach can allow compute resources to start quickly and scale as document volume increases. In one of our deployments, ECS-based resources started in less than a minute and provided flexibility for handling spikes in processing volume.
Amazon SageMaker is another option. It provides managed infrastructure for building, training and deploying machine-learning models. However, it may not be the best operational or economic fit for every production inference environment. In a different implementation, the team moved from SageMaker to ECS after finding that SageMaker costs were about 40% higher in that environment, new instances could take up to two minutes to start and a 15-instance limit restricted scaling flexibility. Those results are specific to one configuration. They do not establish that ECS is always preferable to SageMaker. They do demonstrate the importance of testing actual workloads against the factors that affect production performance: startup time, concurrent-processing capacity, instance limits, operating expense and internal technical expertise.
Treat AI Extraction as a System
AI document extraction is not a one-time model-selection exercise. It is a production system supporting a business process. The model is only one part of that system. The broader workflow includes document intake, classification, data schemas, validation rules, trusted data sources, exception queues, monitoring, audit trails and human review.
A less capable model operating inside a well-designed validation framework may produce more reliable business results than a powerful model that is deployed without controls. Organizations should also expect to revisit their architecture. Models improve, pricing changes, providers introduce new tools and document volumes shift. A workflow that is financially sound today may not be the best option next year.
The most successful teams create an ongoing measurement process. They track field-level accuracy, exception rates, turnaround time, cost per document, infrastructure utilization and the business impact of errors. They maintain representative test documents and use them to evaluate new models before introducing them into production. The objective is not automation at any cost. It is to apply automation where it is dependable, preserve human oversight where it adds value and create a workflow that can scale without compromising data quality or financial discipline.
Brent Wesler
Brent Wesler is VP of Strategic Technologies & Digital Automation at VRC Companies.