We build AI systems that classify documents by type, topic, priority, and custom categories, routing them to the right workflows without manual sorting.
Organizations process thousands of documents daily: invoices, contracts, correspondence, applications, reports, and compliance documents. Manual sorting and classification of these documents consumes significant staff time and introduces errors that cascade through downstream processes. AI document classification eliminates this manual step, automatically identifying document types and routing them to appropriate workflows with speed and consistency.
AI classification goes beyond simple document type detection. Modern systems can identify the specific sub-type of a contract, assess the urgency of correspondence, detect the department a document pertains to, and flag documents that require special handling. This multi-dimensional classification enables sophisticated routing and processing workflows that would be impractical with manual sorting.
Arthiq builds document classification systems that handle the full range of enterprise documents. Our experience with InvoiceRunner has given us practical expertise in classifying financial documents, and we extend this capability to legal documents, HR forms, medical records, insurance claims, and any other document type your organization processes.
Effective document classification requires understanding both the visual layout and textual content of documents. Some documents are identifiable by their layout: invoices have distinctive structures, tax forms follow standardized formats. Others require reading the content: a letter might be a complaint, a request, or a confirmation, distinguishable only by what it says.
Arthiq builds multi-modal classification systems that analyze both visual and textual features. For layout-based classification, we use document layout models that understand the spatial arrangement of text, tables, and images. For content-based classification, we use LLMs and fine-tuned text classifiers that understand the semantic meaning of the document content.
Our training process is designed for efficiency. We start with pre-trained models that already understand document structures and language, then fine-tune with your specific document categories using relatively small labeled datasets, typically 50 to 200 examples per category. For categories where labeled data is scarce, we use few-shot LLM classification that can achieve reasonable accuracy with as few as five examples.
Documents often need to be classified along multiple dimensions simultaneously. A single document might need classification by document type, business unit, urgency level, sensitivity, and required action. Arthiq builds multi-label classification systems that assign multiple categories from different taxonomies in a single processing step.
Classification results drive automated routing decisions. An urgent contract amendment is routed to the legal team with high priority. A routine vendor invoice is queued for standard accounts payable processing. A compliance document is sent to the compliance team with the specific regulation it pertains to. Each routing rule is configurable by your team and can be updated without engineering changes.
We also implement exception handling for documents that do not fit neatly into existing categories. Rather than forcing a classification, the system flags ambiguous documents for human review, providing its best-guess classification and confidence scores to assist the reviewer. These human decisions feed back into the model as training data, progressively expanding the system capability.
Production document classification systems must handle high volumes while maintaining accuracy. Our systems process thousands of documents per hour with horizontal scaling that adjusts to workload demands. Classification latency is typically under two seconds per document, enabling real-time processing even during peak volumes.
Accuracy monitoring runs continuously in production. We sample classified documents for human verification, tracking accuracy by category and flagging any categories where performance degrades. When accuracy drops below acceptable levels, we investigate the cause and retrain or adjust the model accordingly.
The system gets smarter over time. Human corrections during exception handling generate new training data. Documents from new sources or with new formats are analyzed and incorporated into the model through periodic retraining cycles. Category definitions can be refined as your organization needs evolve, with the classification model adapting to the updated taxonomy.
Document classification is a foundational capability that enables downstream automation. Once documents are correctly classified and routed, every subsequent step in your document workflow becomes easier to automate. Arthiq builds classification as the first stage of comprehensive document processing pipelines.
We deliver classification projects with clear accuracy targets, measured against your actual documents. Our iterative approach validates accuracy at each stage, starting with your highest-volume document types and expanding progressively.
Contact us at founders@arthiq.co to discuss how automated document classification can streamline your document processing workflows and eliminate manual sorting.
Our team will build a classification system that sorts your documents accurately, routes them to the right workflows, and eliminates manual sorting from your processes.