Invoices, contracts, forms — into your systems without typing.
AI pipeline: PDFs and images → structured, validated data → NetSuite, QuickBooks, or your custom system. Monthly retainer delivery.
Who this is for
Ops or finance lead drowning in invoices, contracts, or forms where manual data entry is error-prone, expensive, and the primary source of staff turnover.
The pain today
- Data entry clerks processing hundreds of invoices monthly
- Error rate in manual entry costing time and customer trust
- Staff turnover in data entry roles above 50% annually
- Tried OCR alone, failed on variable layouts
- ERP requires data in its specific format, no easy integration path
The outcome you get
- Automated pipeline: document uploaded → extracted → validated → synced
- Accuracy ~95%+ on standard document types, with confidence scoring
- Human-in-the-loop for low-confidence extractions
- Integration with NetSuite, QuickBooks, SAP, or custom downstream
- Time savings: typical ~40 hours/month of manual processing eliminated
When OCR + LLM beats pure OCR
Traditional OCR (Tesseract, ABBYY, Azure Document Intelligence) reads text from images well. What it can't do: understand variable layouts (invoice from vendor A looks different from vendor B), handle poor scans with typos, infer missing data (line item descriptions, tax categorizations), or validate extracted data against business rules. LLMs augment OCR exactly where it struggles — layout reasoning, error correction, inference. Modern pipeline: OCR extracts raw text + bounding boxes, LLM reads OCR output plus original image (vision-capable models), extracts structured data with confidence per field, pipeline validates against your schema. Accuracy lifts from ~80% (OCR alone) to ~95%+ on mixed document types.
Validation and human-in-the-loop
Automation without validation creates expensive errors at scale. Validation layers. Schema: extracted data matches expected types and ranges (amount is a number, date is valid, vendor exists in your system). Business rules: amount fits expected range for vendor, tax calculation within tolerance, line items sum to total. Confidence: each field has a model-provided confidence score; low scores route to human review queue. Human review UI: reviewer sees original document and extracted data side-by-side, corrections feed back into fine-tuning. For 5% of documents (typical low-confidence rate), human review adds seconds per document — far less than typing full data manually.
Integration to downstream systems
Extracted data has to reach the system of record. NetSuite: SuiteScript or REST API, invoices land as pending bills for AP approval. QuickBooks Online: REST API with direct bill creation. SAP: iDocs or BAPI depending on version, typically through middleware. Custom ERPs: API integration scoped per their capabilities. Critical pattern: the AI pipeline doesn't write directly to GL — it creates draft records that finance approves. This preserves controls (AP approval workflow, spending authority) while eliminating the typing. Monthly close still happens; it just happens faster because most entries are accurate on first draft.
Common use cases
Invoices: accounts payable automation, vendor invoice extraction into ERP. Expense receipts: employee submissions processed automatically with categorization. Contracts: clause extraction, obligation tracking, renewal alerts (overlaps with contract analysis service). Forms: customer intake, medical records, loan applications with extracted data populating downstream systems. Shipping documents: BOL, packing list, commercial invoice extraction for logistics. Claims: insurance claim forms parsed for claims processing. Each has domain-specific validation rules; I build those per client based on their document types and business logic.
Time and cost savings
One client example pattern: 40 hours per month of manual document processing eliminated (SITE-FACTS §9 references this type of outcome). Typical monthly savings: 40 hours × $30/hour loaded = $1,200. At the AI Automation monthly rate of $3,000, the math works when monthly savings exceed the retainer plus LLM API costs (usually $100–500/mo). Most document processing use cases clear this bar when volume is above ~100 documents per month. Below that, staff time + tool retainer doesn't justify — I'll tell you honestly if your volume is below break-even so you don't overspend on automation that doesn't pay back.
Pricing
Document processing automation fits the AI Automation retainer at $3,000/mo. First-version timeline: 4–6 weeks for one document type into one downstream system. Retainer continues through expansion to new document types (each typically 1–2 weeks) and ongoing accuracy tuning. 14-day money-back, cancel anytime, Work Made for Hire. LLM API costs (GPT-4 Vision, Claude 3.7) and OCR costs (Azure Document Intelligence, AWS Textract) billed directly to you — typically $0.10–0.50 per document.
Frequently asked questions
The questions prospects ask before they book.
- What accuracy can I expect?
- Typical 95%+ on structured documents (invoices, standard forms) from consistent vendors. Variable-layout documents (contracts with clause extraction) land at 85–90% and benefit more from human review. Baseline depends on document quality — scanned-to-PDF documents at 300 DPI work well; camera-captured smartphone photos need pre-processing.
- Can it handle handwritten documents?
- Yes, but accuracy drops. Printed/typed: 95%+. Handwritten forms with predictable field locations: 80–90%. Handwritten free-text (doctor notes, repair orders): 70–85%. For handwritten-heavy use cases, I recommend evaluating specialized handwriting OCR services (Google Document AI, Azure Handwriting) as part of the pipeline.
- How do you handle PII and sensitive documents?
- LLM providers (OpenAI Enterprise, Anthropic, Azure) offer data processing agreements. PII fields can be redacted before sending to LLM when full content isn't needed. Data-residency-sensitive deployments can use Azure OpenAI in specific regions or self-hosted models (Llama via Groq). Scoped per your compliance requirements.
- What if a document type changes?
- The pipeline adapts to variation naturally — LLM handles minor changes (new field appears, layout shifts). Major changes (new vendor format, new document type) are a 1–2 week extension to add the type, train validation rules, and verify accuracy. Most clients add new document types every 1–2 months as needs expand.
- Does finance still control approval?
- Yes. The pipeline creates draft records that your AP or finance workflow approves. Existing approval chains (manager approval for amounts over threshold, PO matching, three-way match) stay in place. Automation eliminates the typing; it doesn't eliminate the judgment or controls.
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