At $15–$25 per manually processed document (labor + error correction), a company processing 500 POs and 500 invoices monthly saves $180K–$300K annually. Most deployments pay for themselves within 4–6 months.

AI & Machine Learning · Document Intelligence

AI Document Intelligence

Q: What extraction accuracy can we expect?

Typical results: 90–97% field-level accuracy out of the gate for well-structured documents (typed PDFs with consistent formats). Accuracy improves to 95–99% after model tuning on your specific document formats. Low-confidence extractions are routed for human review.

Q: Does this work with our ERP?

Yes. We integrate with modern and legacy ERP systems. Extracted data maps to your ERP's field structure and enters through the ERP API or Python-based integration middleware.

Stop paying people to type numbers from one system into another. Operationally complex businesses still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.

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The Problem

Manual Data Entry That Shouldn't Still Exist

The problem is rarely that the model cannot generate an answer. The real problem is that the data, permissions, exception rules, and action boundaries are not governed well enough for AI to affect production work.

What leaders see

Promising pilots that do not change daily work.

Teams test tools, get useful output, and still copy results into spreadsheets, tickets, emails, or ERP screens by hand.

What is actually happening

The automation cannot reach the operating record.

Source data, permissions, business rules, exception handling, and audit trails are not clean enough for the system to take action.

What gets worse

Automation scales uncertainty.

Bad inputs move faster, decisions become harder to trace, and teams lose confidence before AI becomes operationally useful.

What Changes

What AI Document Intelligence includes.

Operationally complex businesses still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.

Purchase Order Extraction

Automatically extract line items, quantities, pricing, ship-to addresses, and terms from incoming POs in any format — PDF, email, fax image, EDI. Validate against your item master and pricing rules before entry.

Invoice Processing

Extract vendor, line items, amounts, PO references, and payment terms from supplier invoices. Three-way match against PO and receipt automatically. Route exceptions for review instead of manually matching every invoice.

RFQ & Spec Sheet Parsing

Extract technical requirements, material specifications, quantities, and tolerances from RFQs and engineering drawings. Structure the data so your estimating team starts with a populated template instead of a blank screen.

BOL & Shipping Document Processing

Extract shipment data from bills of lading, packing slips, and carrier documents. Automatically reconcile against expected shipments and flag discrepancies.

Multi-Format & Multi-Language

Handle PDFs, scanned images, email bodies, Excel attachments, and handwritten forms. Support for multi-language documents common in international supply chains.

ERP Auto-Entry with Validation

Extracted data flows into your ERP with configurable validation rules — item number verification, price tolerance checks, quantity limits, and customer account validation. Errors are caught before entry, not after.

How It Fits Your Operations

How AI Document Intelligence fits your operation.

Intelligence layerWhich decisions can be automated, which need review, and which should stay human-owned.

Governance dependencyThe agent needs governed inputs, clear action boundaries, and audit logging before it can touch production workflows.

Data the model must trust

ERP history

exception queues

pricing rules

quality records

fulfillment events

What Launchpad defines before engineering begins

What the business needs to change and why.
Which systems, records, risks, and readiness gaps shape the work.
What should be built, how it fits the architecture, and in what order.

Related Foundations

What to evaluate next.

Follow the dependencies behind this service instead of treating it as an isolated project.

Data engineering and integration

Build the governed records, pipelines, and definitions AI needs to produce reliable results.

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Cloud architecture and operations

Prepare secure infrastructure, access controls, monitoring, and recovery for production AI workloads.

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AI on Your Data readiness

Evaluate the use case, owned data, permissions, review model, and workflow outcome before implementation.

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Launchpad readiness and Roadmap

Assess readiness, dependencies, risk, architecture, and implementation order before engineering begins.

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Launchpad Before Engineering

Decide what to build and in what order.

Launchpad assesses the business and turns discovery into priorities, risks, readiness, architecture, and an implementation Roadmap. Metrotechs then engineers and supports the approved solution.

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Metrotechs designs, builds, integrates, and supports the approved solution.

Launchpad keeps priorities, risks, owners, decisions, and delivery governance connected.

Delivery sequence

How Metrotechs delivers AI Document Intelligence.

Operationally complex businesses still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors.

Document Workflow Audit

Inventory all document types processed manually — POs, invoices, RFQs, BOLs, spec sheets. Quantify volume, error rates, processing time, and cost per document. Prioritize by ROI.

Template & Model Training

Configure extraction templates for your most common document formats and train models on your specific document variations. Each supplier and customer may format documents differently — the model handles it.

Validation Rules & Business Logic

Define the validation rules that catch errors before data enters your ERP — item master lookup, price tolerance, quantity checks, customer/vendor verification, and duplicate detection.

ERP Integration & Routing

Connect extraction outputs to your ERP's order entry, AP, or purchasing modules. Configure exception routing for low-confidence extractions or validation failures.

Production & Accuracy Improvement

Deploy with accuracy tracking, exception dashboards, and continuous model updates. Extraction accuracy improves as the model processes more of your specific document formats.

FAQ

Questions that usually decide the scope.

Straight answers to what operators ask before committing budget to this work.

What document formats can you process?

PDF (native and scanned), email bodies, Excel/CSV attachments, TIFF/JPG images (including fax), Word documents, and EDI. If your team currently reads it and types data from it, we can automate the extraction.

What extraction accuracy can we expect?

Does this work with our ERP?

What's the ROI?