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Your accounting team manually keys data from PDF invoices, purchase orders, and contracts into QuickBooks or Siigo — 200–500 times per month, 3–5 minutes each, with a 3–5% error rate. Every mistake costs $50–$100 to fix. An AI document processing pipeline extracts, validates, and writes data directly to your ERP — 80–90% less manual entry. Deployed in 2 weeks.
2-week scoped pilot. Delivered to production. Measurable ROI on day 14.
These are the operational bottlenecks that AI automation eliminates in a 2-week pilot.
Your team spends 10–30 hours per week manually typing data from PDFs into your accounting system — 200–500 documents per month. During vacations or sick days, documents pile up and payments get delayed
Manual data entry has a 3–5% error rate — every error costs $50–$100 to fix. A single mistyped amount can mean paying the wrong vendor, missing a tax deadline, or double-paying an invoice
Enterprise OCR platforms like ABBYY and Rossum cost $500–$5,000/month and take 2–4 months to implement. Offshore data entry (BPO) costs $3–$8/hour — same slow speed, same error rate, plus data security risk and communication overhead
An intelligent document processing pipeline using AWS Textract for OCR — handles PDFs, scanned images, and photos. LangChain semantic extraction understands document layout variation: it finds "vendor name near the top," "total amount near the bottom," "line items in a table" — not fixed positions. Validation rules engine checks: amount matches sum of line items, vendor exists in your system, PO number is valid. Documents that pass validation (confidence >90%) are written directly to QuickBooks, Siigo, Alegra, SAP Business One, or Odoo — with a full audit trail. Documents with low confidence or failed validations go to a human review queue: the reviewer sees the original document with problem areas highlighted, the extracted data, and the specific issue — one click to approve or correct. Processing time drops from 3–5 minutes per document to under 30 seconds. Error rate drops from 3–5% to under 1%. You own the pipeline (Apache 2.0).
Every 2-week pilot includes these five modules — delivered, tested, and ready to use.
Cloud-based OCR configured for your document types — invoices, purchase orders, contracts. Handles PDF, scanned images, and photos. Preprocessing layer for quality normalization. Documents processed in your AWS region (sa-east-1 default for LatAm).
Field-level extraction using LangChain semantic parsing. Handles format variation — no two invoices look the same, and that is fine. Extracts vendor name, amount, date, line items (invoice), supplier and quantities (PO), parties and key clauses (contract).
"Amount matches sum of line items." "Vendor exists in your system." "PO number is valid." Documents that fail validation or have confidence <90% on any field are flagged for human review before write-back. You define the rules.
Web-based dashboard for reviewing low-confidence extractions and validation failures. Reviewer sees: original document with problem areas highlighted, extracted data, and the specific issue. One click to approve or correct.
Validated data written directly to QuickBooks, Siigo, Alegra, SAP Business One, or Odoo. Full audit trail per document — who reviewed, what was corrected, when it was written. Processing time: under 30 seconds per document.
No long discovery phases. No open-ended retainers. A clear scope, a fixed timeline, a measurable result.
30 minutes. We define your document types, extraction fields, destination systems, and current manual volume. By the end of the call, we can tell you whether this is a 2-week pilot — and exactly what it would cost.
2–5 business days. Document quality audit: test OCR accuracy on your real documents. Configure extraction rules, validation logic, ERP write-back. Define 2–3 document types for the pilot. Written scope with fixed price — you approve before any work starts.
2 weeks. Your document AI pipeline goes live. Day 5: first document type processing — OCR extraction working. Day 10: validation rules + human review queue. Day 14: all 2–3 document types, ERP write-back, and audit log delivered.
We measure against agreed success metrics: manual entry reduction (80–90% target), processing time per document, extraction accuracy, error rate. Pre-pipeline baseline vs. post-pipeline result. Then scope Phase 2: new document types, real-time intake, or deep ERP integration.
Full documentation, team training on the review queue and pipeline, source code (Apache 2.0). You own the processing pipeline. Optional retainer for new document types, extraction tuning, and ERP connector maintenance.
AWS Textract + LangChain pipeline eliminates 80–90% of manual data entry — same extraction architecture proven in HunterX and SynapChain.
Invoices, purchase orders, and contracts auto-processed — only exceptions (confidence <90%) go to human review.
From 3–5 minutes of manual typing to automated extraction, validation, and ERP write-back — including human review for exceptions.
Down from 3–5% manual entry error rate. Validation rules catch mismatches before data reaches your ERP.
Book a free 30-minute discovery call and we will scope your first automation with clear deliverables and measurable outcomes.
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