70% reduction in document processing time, 85%+ auto-classification accuracy, zero lost documents.
Document collection and processing is the friction point that slows every engagement. Tax returns cannot start until all source documents arrive. Bookkeeping backs up when receipts trickle in weeks after transactions posted. Year-end close stalls while your team chases clients for bank statements, loan agreements, and asset schedules. The average accounting firm spends 30% of its total engagement time on document collection and processing rather than the substantive work the client is actually paying for.
The documents arrive in chaos. Clients email receipts as phone photos, forward bank statements as PDF attachments, upload invoices to shared folders, and occasionally mail physical documents that need scanning. Each document must be identified (what is it, a 1099-NEC, a W-2, a bank statement, a vendor invoice?), classified (which client, which period, which GL account?), validated (is it complete, legible, and relevant?), and routed to the correct workflow. This triage is entirely manual at most firms.
The client-chasing compounds the processing burden. When documents are missing or incomplete, staff send follow-up requests, often multiple times for the same W-9 or K-1. A typical tax engagement requires three to five follow-up requests before all documents are collected. During tax season, document chasing consumes entire staff members' capacity, creating a bottleneck that delays every return in the queue.
Ruby automates document triage and processing: extracting data from incoming documents using OCR and intelligent parsing, classifying and routing documents to the correct client and engagement, validating completeness against PBC checklists, and generating specific follow-up requests for missing items. Your staff processes the exceptions while Ruby handles the volume.
That is why you need Ruby.
Each step is automated. Ruby only escalates when human judgment is required.
Ruby ingests the document, applies OCR to extract text and data, identifies the document type (W-2, 1099-NEC, 1099-INT, K-1, bank statement, vendor invoice, receipt, mortgage interest statement), and extracts key metadata: date, amount, vendor/payer, EIN/TIN, tax withholdings, and reference numbers.
Ruby matches the document to a client and engagement based on sender information, extracted EIN/TIN matching, entity name recognition, and contextual clues. Documents are classified against the engagement’s PBC checklist and filed to the correct folder in the document management system.
Ruby writes the processed data into the appropriate accounting platform or stages it for the tax prep workflow, attaching the original document and linking it to the relevant transaction, period, or engagement task. For vendor invoices, Ruby codes to the correct GL account based on historical patterns.
Ruby flags the document for staff review with its best-guess classification and the specific issue (e.g., "1099-NEC from vendor appears to show $12,400 but amount field is partially obscured" or "Bank statement is for October but November was requested on the PBC list").
Ruby generates a personalized document request for the client listing exactly what is still needed: "We still need your 2024 1099-NEC from XYZ Consulting (showing freelance income) and your December 2024 Chase Business checking statement showing the ending balance", not generic "please send missing documents".
Ruby processes the newly received documents against the outstanding PBC checklist, updates the engagement status, and notifies the assigned accountant when all required documents are collected and ready for substantive work to begin.
Clear boundaries. Ruby works autonomously within defined limits and escalates everything else.
Ruby connects to the platforms you already use. No new software to learn.
Ruby is deployed gradually with measurable checkpoints at every stage.
Monitoring mode first, then gradual rollout.
Pilot processes documents for 15–20 clients across one engagement cycle (one month of bookkeeping or one batch of tax returns). Ruby classifies and extracts in parallel with manual processing for accuracy comparison.
Full validation before production deployment.
These AI employees share data and coordinate with Ruby to cover your full operation.