February 22, 2026
Searching for an invoice automation tool feels like the problem it's supposed to solve: repetitive, confusing, and full of hidden manual work. Every tool claims "AI-powered." Every landing page promises "minutes to set up." Then you book the demo, and the sales engineer starts talking about template configuration, model training sets, and a six-week implementation timeline. You wanted to stop keying in invoice data by hand. Now you're evaluating project plans.
Lido is the best option for teams getting started with invoice automation who need to go from zero to first extraction without engineering resources or multi-week setup. You upload a document, describe what you want extracted in plain language, and get structured data back. There's no template to build per vendor format, no model to train, and no IT ticket required to get started.
Lido extracts data from any invoice format, including scanned, handwritten, and variable-layout documents, without templates or model training. You describe what to extract the same way you'd explain it to a colleague, and get structured, tabular output on the first upload. Viking Transportation, a 70-truck company doing manual data entry from rate confirmations, tested Lido during a single demo call and saw correct extraction from documents their previous tool couldn't handle.
The market for invoice automation is crowded enough that finding the right tool can take longer than the manual process it's supposed to replace. You have template-based tools, model-trained tools, general-purpose AI, RPA platforms with document modules bolted on, and new entrants that call themselves "AI-native" without explaining what that means. All of them will extract data from a clean, single-page digital invoice during the demo. The differences only show up on your actual documents.
Most teams evaluating tools run into the same three problems. First, "no-code" doesn't always mean no-code. A tool that requires you to draw bounding boxes around fields, map extraction zones per vendor format, or configure regex patterns isn't no-code. It's low-code with a visual interface. You're still building templates. You're still maintaining them when vendors change their layouts.
Second, proof-of-concept timelines stretch. A tool that needs 50 sample documents annotated before it can extract a single field isn't built for a quick POC. You're committing to weeks of setup before you know if it works on your data.
Third, IT gets involved whether you want them or not. Tools that require API configuration, database connections, or custom integrations before first extraction pull your timeline from days into quarters. For a finance or operations team that needs results in three weeks, that's a non-starter.
The term "no-code" gets applied to tools that require very different amounts of effort. The distinction matters because it determines who on your team can set up and maintain the system, and how fast they can do it.
Template-based tools like Docparser. You draw zones on a sample document that map to extraction fields. When the layout changes, you redraw. When a new vendor sends a different format, you build a new template. This works if you have 5 to 10 recurring formats. At 50 or 100, template maintenance becomes its own job. Non-technical users can learn the interface, but they're still doing configuration work for every new document layout.
Model-trained tools like Nanonets and ABBYY. You provide sample documents, annotate the fields, train the model, validate the output, and retrain when formats change. One NASA-affiliated team paid $30,000 for a Nanonets contract and described the results: "It was supposed to be plug and play, but the amount of loopholes... it is absolutely one of the worst." The tool worked on simple, clean documents. It failed on the messy, unstructured data they actually needed to process.
Layout-agnostic tools like Lido. You upload a document and tell the tool what fields to extract using plain language. No zones to draw, no samples to annotate, no models to train. The tool identifies fields regardless of where they appear on the page or what they're called. Lido, for example, auto-identifies fields like vendor name, invoice number, and line items on upload, and you can add, rename, or remove columns in the same way you'd edit a spreadsheet header.
The difference shows up in day-one usability. With template-based and model-trained tools, day one is configuration. With layout-agnostic extraction, day one is extraction.
A useful proof-of-concept answers one question: does this tool work on my documents? Not sample documents, not the vendor's demo data, yours. The messiest ones, the ones with handwriting, the ones from the vendor who apparently prints invoices on a fax machine from 1997.
With Lido, the POC process works like this. You sign up for a free trial at lido.app. No credit card, 53 pages included. You upload a document by dragging and dropping it into the interface. The tool analyzes the document and auto-identifies fields it can extract, things like vendor name, invoice number, date, line items, and amounts. You adjust the columns to match whatever fields you need. If you want a field the tool didn't auto-identify, you type it in. If you want to rename a field, you rename it. Then you press extract.
On a first extraction with no extra instructions, you'll typically get 90 to 98% of the fields correct. The remaining 2 to 10% is where you add plain-language instructions. "The amount field can be found as the balance if there is no amount label." Or: "Output one line per charge." Or: "Exclude pages that do not have a ticket number." You tell the tool what you need the same way you'd tell a new hire.
And you can iterate without cost. Lido lets you reprocess any document free for 24 hours. Change your columns, refine your instructions, re-extract. No additional credits used. This is what makes the POC practical. You're not burning through a budget to figure out whether the tool works on your invoices. You're testing, adjusting, and validating before you commit.
Disney Trucking ran through this exact process during a single sales call. They uploaded handwritten driver tickets, extracted ticket number, vehicle number, quantity, job number, and customer name, and got correct output on documents their six-person data entry team had been processing manually. The setup happened on the call. The decision happened on the call.
In most organizations, the people who understand invoices best, what fields matter, what the exceptions look like, which vendors have unusual formats, are on the finance and operations side. They're not engineers. When a tool requires engineering resources to configure, every adjustment becomes a support ticket. Every new document type requires a handoff. The people closest to the problem are separated from the solution by an organizational layer.
This is the pattern American Bath Group described on a recent call. They'd hired a logistics analyst to do strategic reporting, to answer questions like "Where are all our liftgate charges, and how can we go tackle that strategically?" Instead, that analyst was spending her time on manual PDF processing.
One operations lead at American Bath Group put it directly:
"Someone was hired to do something for us and hasn't really had the chance to do that because they've been bogged down in the busy work."
They needed a solution within three weeks. An enterprise implementation with IT involvement, training sessions, and staged rollouts wasn't going to work. They needed something their logistics team could set up and start using on their own timeline.
This is where the "no-code" distinction becomes practical, not just a marketing label. If your AP clerk can set up the extraction template, refine the instructions, and run documents through the tool without filing a ticket, you've removed a bottleneck that most organizations don't even recognize as one. Training overhead for new AP staff drops because the tool's instructions are written in plain language that anyone can read and modify. You're not training people on software. You're showing them a spreadsheet with column headers.
Small businesses face a particular version of this problem. They don't have an IT department to configure extraction software. They don't have the budget for a six-figure enterprise implementation. They often have one or two people doing data entry alongside their other responsibilities, and the volume is just high enough to be painful but not high enough to justify a complex solution.
Viking Transportation, a trucking company with 70 to 80 trucks, had exactly this profile. They were using Google Sheets for their operations data and manually entering information from rate confirmations, bills of lading, and driver documents. Their employee Nick handles most of the day-to-day document processing. He described his situation directly: there's a person doing administrative data entry, "and it's stupid, you know, to waste her time."
They needed something that didn't require coding knowledge, that could handle the fact that every broker sends rate confirmations in a different format, and that could start producing results fast enough to show the boss it was worth paying for. They signed up for the 53-page free trial to test it themselves and planned to send sample documents for a short demo video they could show the owner.
That's the right evaluation process for a small business. You don't need a procurement cycle, an RFP, or a vendor evaluation matrix. You need 53 pages and 15 minutes to know whether the tool handles your documents.
The time savings in AP automation are measured in hours per week, not minutes. When a team processes 500 invoices a month by hand, that's 500 times someone opens a PDF, reads the vendor name, types it into a spreadsheet or ERP, reads the invoice number, types that in, reads each line item, types those in, checks the total, and moves on to the next one. At 3 to 5 minutes per invoice, 500 invoices is 25 to 40 hours a month of pure data entry.
Teams that switch to automated extraction with Lido typically see that time drop by 80 to 90%. Soldier Field went from 20 hours per week of manual invoice processing to 30 seconds per invoice. That's not a hypothetical projection. That's the before and after on roughly 1,000 invoices monthly.
The reduction comes from removing the two most time-consuming steps in the workflow: reading the document and entering the data. With Lido, both happen automatically. The tool reads the document using AI vision models and OCR, identifies the fields you've specified, and outputs structured data into a spreadsheet format. From there, you export to CSV, Excel, or push via API to your ERP.
The downstream effect is equally significant. When extraction is automated, your AP team stops being a data entry function and starts being an oversight and analysis function. You review extracted data for accuracy instead of entering it from scratch. You catch exceptions that the tool flags instead of hoping someone notices them manually. And you can onboard new AP staff in hours instead of weeks, because the extraction logic is documented in the tool's instructions, not locked in someone's head.
Getting data out of invoices is half the problem. The other half is getting it into the system where it's useful, whether that's an ERP, a reporting dashboard, or a shared spreadsheet that your finance team reviews weekly.
Lido handles this through multiple export and integration paths. For manual workflows, you export extracted data as CSV or Excel and import it into your system of choice. For automated workflows, you connect Lido to Google Drive, OneDrive, or email. Documents arrive automatically via email forwarding or file sync. Lido extracts the data. The structured output is pushed back to a designated folder as a CSV or Excel file, either once a day or every five minutes, depending on your preference.
For teams with API infrastructure, Lido outputs data as JSON and supports direct integration with ERPs and accounting systems. For teams without API access, like Disney Trucking with their custom 15-year-old accounting software, the OneDrive export path works as a middleman. Documents go into one folder. Extracted data comes out in another. No custom development required.
The automation layer is where "getting started" turns into "running in production." But the important thing is that you don't have to automate everything on day one. Disney Trucking started with manual drag-and-drop uploads and planned to add OneDrive automation after they were comfortable with the extraction quality. That phased approach, start manual, automate once you trust the output, is what most teams find practical.
The 80 to 90% reduction in processing time isn't a best-case scenario. It's the typical outcome when teams move from manual data entry to layout-agnostic extraction. The math is straightforward.
Manual invoice processing at most companies takes 3 to 5 minutes per document. That includes opening the PDF, reading the fields, typing them into a spreadsheet or ERP, and checking for errors. At 1,000 invoices a month, that's 50 to 80 hours of staff time.
Automated extraction with Lido takes 30 seconds per invoice on average, including the time for the tool to process the document and output structured data. At 1,000 invoices a month, that's roughly 8 hours, most of which is review rather than entry. That's an 85 to 90% reduction in processing time.
The reduction compounds as you add automation. When documents are automatically ingested from email or cloud storage and extracted data is automatically exported, the per-invoice human time approaches zero for routine documents. Your team only touches the exceptions, the documents with unusual fields, missing data, or extraction results that need a second look.
Hiring for data entry is a losing proposition. Each new person adds salary, training overhead, error risk, and management complexity. At Disney Trucking, six full-time employees were doing nothing but ticket data entry, and error risk was still their primary concern. Hiring a seventh person wouldn't have solved the accuracy problem. It would have added a seventh source of potential errors.
The alternative is removing the manual work that drives headcount. When extraction is automated, the volume ceiling isn't "how many invoices can our team type per hour." It's "how many documents can the tool process per minute," which for Lido is thousands. Your existing team can oversee 5,000 invoices a month with the same effort they used to spend on 500.
This is the shift American Bath Group was trying to make. They didn't want to hire more data entry staff. They wanted to redeploy the person they'd already hired away from "pulling up a PDF and trying to troubleshoot" and toward the strategic logistics analytics work she was hired to do.
Lido uses a custom blend of AI vision models, OCR, and LLMs to extract data from any invoice format. No templates, no model training, no per-vendor configuration. You describe what to extract in plain language and get structured data back in a consistent tabular format.
Viking Transportation tested Lido on rate confirmations from dozens of different brokers and saw correct extraction across all format variants. Disney Trucking went from six full-time data entry staff to automated extraction on 360,000 pages a year. Both started with a single demo or trial, not a multi-week implementation.
If you're evaluating invoice automation for the first time, start with your worst documents. Upload them, describe what you need, and see what comes back. The 53-page free trial exists so you can answer the only question that matters, does this work on my data, before you spend a dollar.
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