ViBe://AUTOMATE
Extract structured data from every document your business receives.
Your team receives PDFs, invoices, contracts, and forms — then manually reads them, extracts the relevant fields, and types that information into a system. Every step of that process can be automated.
Current manual process
How your team handles documents today.
Document processing is time-consuming precisely because it looks simple — but opening, reading, extracting, validating, and entering data at scale adds up to significant hours every week.
- Receive document via email, portal upload, or physical scan
- Open and read to identify document type and relevant fields
- Manually extract key data — amounts, dates, parties, line items
- Cross-reference against existing records for validation
- Enter extracted data into the target system field by field
- File the original document and update any associated records
Cost of manual processing
6–12 hours per week, plus error correction.
Manual data entry isn't just slow — it introduces errors that compound downstream.
- Average 3–8 minutes per document for manual processing
- Error rates in manual data entry typically run 1–5% of fields
- Downstream errors in payments, contracts, or records require correction time
- Processing backlogs during high-volume periods
- No standardized validation — different people catch different things
Automated flow
Receive → Extract → Validate → Update.
Human checkpoints
Where your team verifies.
- Low-confidence extractions below your defined accuracy threshold
- Validation failures — mismatches against existing records or PO data
- New document formats from unfamiliar sources
- High-value documents above a configurable dollar threshold
- Exception reports reviewed weekly — patterns flagged for rule improvement
Systems involved
Source and destination systems.
Email / portal
Document intake via email attachment or upload portal.
Accounting software
QuickBooks, Xero, NetSuite — destination for invoice and payment data.
ERP
PO and contract data for validation cross-reference.
Document storage
Google Drive, SharePoint — archive with extracted metadata.
Contract management
Extracted contract terms written to contract records.
CRM
Client-related documents linked to account records.
Expected capacity recovery
Illustrative weekly time savings.
Per week recovered
6–12 hrs
Illustrative estimate
Implementation time
2–4 wks
From kickoff to live
Per-document processing time
<60 sec
vs. 3–8 min manual
Capacity estimates are illustrative ranges based on typical document volume and format mix. Actual hours recovered depend on document types in scope and validation complexity.
Implementation
Live in 2–4 weeks.
01
Audit
Catalog document types, sources, fields extracted, and target systems.
02
Build
Configure extraction templates, field mappings, validation rules, and destination integrations.
03
Pilot
Run on a sample of recent documents. Validate extraction accuracy against manual benchmark.
04
Full operation
Process all incoming documents automatically. Monitor exception rate and accuracy weekly.
FAQ
Common questions.
How does it handle handwritten or low-quality documents?+
OCR and extraction accuracy varies by document quality. Low-confidence extractions are flagged for human verification before any data is written to your system. You set the confidence threshold during setup.
Can it handle different invoice formats from different vendors?+
Yes. The system learns the layout patterns of recurring document sources over the pilot period. New or irregular formats are flagged for human review until a pattern is established.
What systems does extracted data get written to?+
Any system with an API — accounting software, ERP, CRM, contract management, or a custom database. We configure the field mappings during the build phase.
Get started
Automate your document processing.
Book a demo to walk through your current document volume and target systems. We'll scope a build plan with extraction templates, validation rules, and a go-live timeline.