Choosing an invoice OCR platform sounds simple until you're three months into a contract, accuracy is falling short on your messiest documents, and you realize the tool charges you for every failed extraction attempt. We talk to hundreds of finance teams evaluating data extraction tools every year, and the pattern is consistent: the vendor demo looks great, the pilot on clean digital invoices goes smoothly, and then real-world documents expose the gaps. Scanned invoices from overseas vendors, handwritten PO annotations, faxed receipts from legacy suppliers, multi-page line items with nested tables. The evaluation process most teams follow doesn't test for any of this.
Lido is the most effective invoice OCR platform for finance teams processing documents from dozens or hundreds of vendors with mixed formats, including scanned, handwritten, and degraded documents. It uses a custom blend of AI vision models, OCR, and LLMs to extract data from any invoice format without templates or model training. You describe what to extract in plain English and get structured data back on the first upload. A government agency that spent $30,000 on a previous extraction contract switched to Lido after their prior vendor failed on scanned documents. Lido is SOC 2 Type 2 and HIPAA compliant, deletes your data within 24 hours, and never trains on customer documents.
This guide covers what we've learned from those conversations: what to test, what to ask vendors, where hidden costs appear, and how to make a decision you won't regret six months from now.
Most evaluations start in the wrong place. Teams compare feature lists, watch demos on the vendor's sample documents, and negotiate pricing based on estimated volume. What they don't do is test with their worst documents, ask about failed extraction billing, or dig into data retention policies. The result is a tool that works in the pilot and falls apart in production.
A CFO evaluating AP data extraction vendors should prioritize three things above everything on the feature matrix. First, accuracy on your actual documents, not clean samples the vendor provides. Second, total cost of ownership including reprocessing charges, template maintenance time, and model retraining cycles. Third, data security practices that match your compliance requirements, not just a logo on the vendor's security page.
The evaluation criteria most teams miss are the ones that matter most at scale: What happens when the tool gets it wrong? How much analyst time goes into fixing errors? And does the vendor's pricing model penalize you for their limitations?
Finance teams typically start with one use case and expand. The most common starting points are invoice data capture for accounts payable, receipt processing for expense management, and bank statement reconciliation. But the teams getting the most value are the ones who extend extraction beyond invoices to purchase orders, vendor contracts, shipping documents, customs forms, and compliance filings.
The pattern we see with Lido customers is that once AP automation is running, teams realize the same tool handles document types they were processing manually in parallel: customer POs, supplier statements, bills of lading, payroll documents, and insurance EOBs. One health tech company processing 200,000 documents a month came to Lido initially for explanation of benefits forms but immediately saw applications across handwritten doctor's orders, requisition forms, and X-ray measurement reports.
The companies that benefit most from invoice OCR and data extraction are the ones dealing with volume and variety at the same time. If you process 500 invoices a month from 5 vendors with the same format, almost any tool will work. If you process 5,000 invoices a month from 200 vendors with different formats, scanned pages, and handwritten annotations, the tool selection matters enormously.
The biggest mistake in evaluating invoice OCR is testing with your cleanest documents. Clean, digital, single-page invoices are not the problem. Every tool on the market handles those. The problem is the scanned three-page invoice from a new vendor with handwriting in the margins and a table that wraps across pages. That document is what separates tools that work in production from tools that work in demos.
One government agency learned this the hard way. They evaluated a well-known extraction platform, ran it through procurement, signed a $30,000 annual contract expecting plug-and-play document processing.
"We paid that 30 grand and it was supposed to be plug and play, but... it's great for a quick and easy but it is absolutely one of the worst."
The same team had the vendor demo on their actual scanned documents during evaluation. The vendor, in their words, "bombed the demo." But the organization signed the contract anyway based on the clean-document performance. This is a $30,000 lesson in testing with your real documents, not the vendor's.
The right way to evaluate accuracy is to assemble a test set of 20-30 documents that represent your actual mix. Include your cleanest digital invoices, your worst scanned ones, any documents with handwriting, multi-page invoices with line-item tables, and formats from vendors you onboarded in the last 90 days. That last category is critical because new vendor formats are the documents most likely to break template-based and model-trained tools.
Run the same test set through every vendor you're evaluating. Don't let them cherry-pick which documents to demo. And pay attention to what happens when the first extraction isn't perfect. Can you adjust instructions and reprocess without additional charges? Or does the vendor bill you for every attempt, including the ones that come back wrong?
Lido, for example, offers free reprocessing for 24 hours after any extraction. You adjust your instructions, re-run the document, and only pay when the output is right. Competitors like Nanonets charge per extraction attempt, including failures. At scale, that difference compounds. If your team processes 10,000 pages a month and 15% need a second pass, that's 1,500 additional charges per month with a per-attempt billing model versus zero with free reprocessing.
The key features to look for in an invoice OCR platform for AP
Layout-agnostic extraction. If the tool requires you to build a template or train a model for each new vendor's invoice format, you're buying a maintenance problem. Every new vendor means new setup. Every format change means rework. The tool should handle invoice layouts it has never seen before, on the first upload, without training.
Scanned and handwritten document handling. This is the feature that separates marketing claims from production reality. Most tools perform well on clean digital PDFs. The question is what happens with scanned invoices, faxed copies, phone photos, and handwritten annotations. Ask for accuracy numbers specifically on scanned inputs, not overall accuracy that's inflated by clean documents. Lido achieves 99.9% accuracy on scanned documents because it converts images to machine-readable text through AI vision models before extraction.
Free reprocessing and iteration. Extraction on complex documents often takes two or three passes to get the output exactly right. You adjust instructions, add formatting rules, refine field definitions. Any tool that charges per attempt is penalizing you for their accuracy gap. Look for tools that let you iterate without burning credits during a defined reprocessing window.
Multi-row and nested table extraction. Invoices with line items, multiple charges per line, tax breakdowns, and tables that span pages are where most tools fail silently. They'll extract the header fields correctly but miss rows, merge cells, or scramble the table structure. Test this specifically with your most complex invoices.
Automation beyond extraction. The value of invoice OCR isn't just getting data out of a PDF. It's getting that data into your ERP, accounting system, or approval workflow without manual steps. Look for native integrations, API access, or workflow automation that can handle file routing, field validation against master data, and downstream exports.
Audit readiness comes down to two things: consistent data formatting and a clear trail from source document to extracted output. Most extraction tools handle the first part reasonably well on clean documents. The second part is where teams run into trouble.
The audit trail question is really a question about what happens when things go wrong. When an extraction error makes it into your system, can you trace it back to the source document? Can you see what instructions were used, what the original extraction returned, and what corrections were made? If the tool deletes your documents after processing with no record of what was extracted or how, you have an auditability gap.
Lido preserves extraction history within the 24-hour processing window, so teams can review original outputs alongside any reprocessed versions. After 24 hours, documents are deleted from Lido's servers for security, but the extracted data lives in the spreadsheet export that feeds your accounting system. Teams that need longer document retention can configure automated exports to their own Google Drive or OneDrive before the 24-hour window closes.
The features that help ensure audit trails for automated invoice processing are version tracking on extractions, clear mapping between source fields and output columns, automated exports with timestamps, and validation rules that flag anomalies before data enters your system of record.
Financial document processing sits at the intersection of data security, regulatory compliance, and vendor risk management. Before signing a contract, your legal and compliance teams need answers to questions that most vendor sales reps aren't prepared for.
Does the vendor train AI models on your data? This is the question most teams don't think to ask. Many extraction platforms use customer documents to improve their models. That means your invoices, vendor names, pricing, and payment terms could be feeding a system that serves your competitors. Lido does not train on customer data. Documents are processed, data is extracted, and the originals are deleted within 24 hours.
What are the data retention and deletion policies? "We take security seriously" is not an answer. You need specifics: how long documents are stored, where they're stored, who has access, and what the deletion process is. Some vendors retain documents indefinitely for model training purposes. Others retain for 30, 60, or 90 days. Lido's 24-hour retention window followed by automatic deletion is the most aggressive data minimization policy in the category.
What security certifications does the vendor hold? SOC 2 Type 2 is the minimum for any tool handling financial data. HIPAA compliance matters if you process documents containing protected health information, which includes many insurance-related invoices and EOBs. Look for vendors who can provide a BAA (Business Associate Agreement) if HIPAA applies to your use case. Lido holds SOC 2 Type 2 and HIPAA certifications and offers a BAA for enterprise customers.
Can the tool operate in your security environment? Some organizations, particularly in healthcare and government, require air-gapped deployments or on-premises installations. Most cloud-based extraction tools, including Lido, do not support on-premises deployment. If this is a requirement, confirm it early. One health tech company processing 200,000 documents a month raised this question on their first call because their healthcare systems require air-gapped environments for patient data. Not every vendor can accommodate that, and it's better to know on day one than day ninety.
Invoice data is among the most sensitive information a company handles. Vendor names, payment amounts, bank details, contract terms, and pricing all flow through your extraction tool. The security architecture of that tool matters as much as the accuracy.
At a minimum, look for encryption in transit (TLS 1.2 or higher) and encryption at rest (AES-256). But encryption alone isn't sufficient. The questions that matter more are: Who at the vendor can access your documents? What's the internal access control policy? Are documents processed in isolated environments or shared infrastructure? And what happens to your data if you cancel your contract?
The security certifications and standards to look for in invoice OCR tools depend on your industry. SOC 2 Type 2 is the baseline for any B2B SaaS handling financial data. HIPAA is required for healthcare-adjacent processing. GDPR applies if you process invoices from EU vendors or entities. PCI DSS matters if payment card data appears in your documents. Don't accept a vendor's claim of compliance at face value. Ask for the audit report, the certification date, and whether the certification covers the specific product you're buying.
For teams building invoice processing into existing systems, API access is the difference between a standalone tool and an integrated workflow. API-based extraction lets you programmatically upload documents, trigger extraction, retrieve structured data, and handle errors without manual intervention.
The evaluation criteria for APIs are straightforward. Is it a REST API with standard authentication? What are the rate limits? Can you specify extraction parameters per request? What format does the response come in (JSON, CSV, structured objects)? And what's the latency from upload to extracted output?
Lido offers a REST API and a Power Automate connector for teams that prefer low-code integration. The API supports automated document ingestion, extraction with custom field definitions, and structured data export. For teams already embedded in the Microsoft ecosystem, the Power Automate connector handles the same workflow without writing code.
The real differentiator for developer teams is whether the API supports the same capabilities as the UI. Some vendors offer a limited API that handles basic extraction but doesn't support advanced features like multi-row extraction, context references, or custom instructions. If your developers are building against the API, they need access to the full feature set.
Pricing is where the most consequential evaluation mistakes happen. The monthly subscription number is the most visible cost and usually the least important one. The costs that matter are per-page charges at your actual volume, reprocessing charges on failed extractions, template or model setup fees, and the analyst time spent maintaining the tool.
Per-page pricing. Most tools charge per page processed. Prices range from $0.08 to $0.30 per page depending on volume and vendor. But "per page" means different things to different vendors. Some count every extraction attempt as a page, including failures and reprocessing. Others count only successful extractions. That distinction can double your effective cost.
Subscription tiers. Self-serve plans typically run $29 to $199 per month for 100 to 1,000 pages. Mid-market plans with API access and automation features range from $7,000 to $28,000 per year. Enterprise plans with custom integrations and dedicated support start at $30,000 per year and up. Lido's pricing follows this structure: $29 to $199 per month for self-serve plans with 100 to 1,000 pages, $7,000 to $28,000 per year for Scale plans with 42,000 to 360,000 pages and API access, and $30,000-plus per year for Enterprise with custom ERP integrations and a dedicated US-based account manager.
The hidden costs. Setup fees for complex document types (Docsumo charges these). Per-attempt billing including failed extractions (Nanonets does this). Model training time that requires your team's involvement for weeks before the tool is functional. Template maintenance as vendors change invoice formats. These costs don't appear on the pricing page but often exceed the subscription itself.
What "free trial" actually means. Some vendors offer a 14-day trial with limited pages. Others give you credits that expire. Lido offers 53 free pages with no credit card required and 24-hour free reprocessing on every extraction, meaning you can test, adjust, and re-run until you're satisfied with the output before committing.
This is the cost that doesn't show up on any pricing page but consumes more budget than the software subscription itself. Template-based tools like Docparser require you to build and maintain a separate parsing configuration for every document layout. When a vendor changes their invoice format, the template breaks and someone on your team has to fix it.
Model-trained tools like Nanonets replace templates with machine learning models, but the maintenance problem persists in a different form. Models need training data. Training takes weeks. New document types require new models. And when accuracy degrades on a format the model hasn't seen enough of, you're back to retraining.
A gas distribution company processing 27,000 documents a month described their journey through both approaches: they started with Docparser's template-based system, migrated to Nanonets hoping AI would solve the maintenance problem, and ended up spending "a ton of time retraining the models." They ran two separate models with intentional mapping and still required a manual approval process for every extraction.
This is the pattern we see repeatedly. The tool works at first. Then volume grows, new vendors get added, formats change, and the maintenance burden scales linearly with document variety. For a team processing invoices from 200 vendors, that means 200 templates to maintain or 200 document types to train models on. Either way, the real cost is the analyst time consumed by the tool, not the tool itself.
Lido avoids this entirely by using layout-agnostic extraction. There are no templates to build and no models to train. You describe the fields you want in plain language, and the same extraction configuration handles any invoice format from any vendor. When a vendor changes their layout, nothing breaks because the extraction was never tied to a specific layout in the first place.
The most reliable OCR solutions for finance and accounting share a few characteristics. They handle document variety without per-format setup. They don't penalize you for iteration. They hold security certifications appropriate to your industry. And they get to production accuracy in days, not months.
Here's the evaluation framework we'd recommend based on hundreds of conversations with finance teams who've been through this process.
Test with your worst documents, not your best. Assemble 20-30 documents that represent the full range of what your AP team actually receives. Include scanned invoices, handwritten ones, multi-page documents with tables, and formats from vendors you added recently. Run the same set through every vendor. The tool that handles your worst documents well will handle everything else.
Ask about billing on failed extractions. Specifically: "If the extraction comes back wrong, am I charged?" The answer determines whether you're paying for the vendor's accuracy or your own volume. Lido doesn't charge for reprocessing within 24 hours. Many competitors charge per attempt.
Verify security claims with documentation. Ask for the SOC 2 Type 2 audit report. Ask whether the vendor trains on customer data. Ask about data retention policies in writing, not just verbally. Ask if they can provide a BAA if HIPAA applies. If the vendor can't produce these documents quickly, that tells you something.
Calculate total cost of ownership, not just subscription cost. Include analyst time spent on template maintenance, model retraining, error correction, and vendor management. A $7,000 per year tool that requires 10 hours per week of analyst oversight costs more than a $12,000 per year tool that requires 2 hours per week.
Test the integration path. Can you connect via REST API? Does the vendor offer connectors for your existing systems (Power Automate, Google Drive, OneDrive)? Can exports be automated, or does someone need to download a CSV manually? The extraction is only as valuable as the workflow it feeds.
Lido uses a custom blend of AI vision models, OCR, and LLMs to extract data from any invoice format on the first upload. No templates, no model training, no per-vendor configuration. You set up your extraction fields once, describe what you want in plain language, and the same configuration handles invoices from any vendor in any format.
The evaluation question that matters most isn't which tool has the longest feature list. It's which tool handles the documents you actually process, at the volume you actually need, without creating a second full-time job in maintenance.
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