Why Infrrd Isn’t Template-Based: A Smarter Way to Onboard New Document Types

Most document automation vendors build products around fixed templates for major document-heavy industries. That makes onboarding faster at the start. It also helps vendors show quick results when a document format matches what they have already solved for another enterprise. But business documents rarely stay that simple.

A template that worked for one company may not match your fields, layouts, labels, or review rules. Your team may need different data today and more fields next quarter. Once a fixed mold is set up, you often have to adjust your process around the tool instead of the tool adjusting to your documents.

Infrrd takes a different path with Doc Studio. It gives teams a smarter way to create an intelligent data extraction model that fits their document needs without forcing them into a preset structure. In this blog, we discuss why fixed templates fail, how smarter onboarding works, and how Doc Studio helps teams onboard new document types faster.

What Is Template-Based Document Extraction, and Why Does It Break Down?

Template-based document extraction uses a fixed layout, field map, or rule set to pull data from documents. The system expects the same fields to appear in the same place or follow a known pattern. This can work for standard forms with low variation.

The problem starts when real documents change. A vendor may move a field, or a borrower may upload a different version, or an insurer may send a new form layout. A logistics file may arrive as a scan, email, PDF, or image.

When the document does not fit the fixed pattern, the system misses fields, extracts the wrong data, or sends more work to human reviewers.

Why Enterprises Need a Smarter 3-Step Onboarding Model?

Document onboarding is not just a setup task; it is a long-term scalability issue. Every new form, business rule, region, carrier, vendor, or customer can add another document type to the workflow.

The real question is no longer, “Can this tool extract fields?” The better question is, “How fast can this system learn when my documents change?”

Enterprise teams deal with high-variation workflows every day. Mortgage files include pay stubs, tax forms, disclosures, and appraisal documents. Insurance teams process claims, policy forms, loss runs, and ACORD documents. Finance teams handle invoices, receipts, statements, and expense reports. Logistics teams receive Bills of Lading, customs documents, and carrier paperwork in many formats.

Teams that onboard new document types often need speed, control, and repeatable output. A smarter onboarding model helps them move faster without starting from scratch each time.

How Infrrd’s Doc Studio Uses Predictive Analytics to Onboard New Document Types

Doc Studio is Infrrd’s proprietary model, trained across 1M+ document types. It uses predictive analytics to understand new documents with minimal sample training and gives users a simple, no-code setup flow. It follows the 3-step onboarding process below.

Step 1: Input Your Documents

Drop your document into Doc Studio. The system reads the file, analyzes its structure, and proactively extracts the data it understands. Source lines show where each extracted field came from.

Step 2: Review and Select the Fields

Your team reviews the extracted fields and selects what matters for the workflow. You can keep important fields, remove extra ones, and shape the model around the data your process needs.

If Doc Studio does not suggest a field you need, you can ask for it in plain English. The model then looks for that data and adds it to the extraction setup.

Step 3: Save the Model and Reuse It

Once the setup matches your needs, save the model. Future documents with the same extraction needs can run through that model and return the fields you configured.

In this way, Doc Studio helps you build a model that caters exclusively to your needs. 

Why Infrrd’s Approach Is Different from Fixed Templates Vendors?

Infrrd uses a template-agnostic approach to rework how teams onboard new document types. Instead of starting with a prebuilt model, Doc Studio starts with the actual document itself. It studies what is present, suggests the data it can extract, and lets users decide what to keep, and helps them fix their model.

This gives teams control without asking them to write code. Business users can adjust fields, request missing data, and save reusable models without waiting for engineering support.

It also reduces vendor dependency during setup. Teams do not need to wait weeks for a custom template or a new configuration cycle. They can move from long setup timelines to minutes of guided configuration.

Conclusion: Your Documents Should Define the Model, Not the Other Way Around

Infrrd makes that happen for your documents. Document onboarding shouldn't slow your business down. Fixed templates create dependency, rigidity, and recurring rework every time your documents change. Doc Studio takes a fundamentally different approach, starting with your documents, not a preset mold. With predictive analytics trained on over a million document types, it lets your team configure, refine, and reuse extraction models without writing a single line of code. 

Whether you're processing mortgage files, insurance claims, or logistics paperwork, Doc Studio adapts to your workflow, not the other way around. Smarter onboarding isn't a feature. It's the foundation of scalable document automation.