Data Entry Automation Done Right: Lessons From Vertafore's Agents

Vertafore, the insurance software company behind AMS360, made two AI agents generally available this week. One reads policy documents and fills in the agency management system. The other compares issued policies against what's already on file and flags anything that doesn't match. Early adopters reported up to 87% less time building and reviewing a standard commercial package.

Insurance is a niche, but the pattern isn't. This is data entry automation done the careful way: the agent proposes, a person approves, and every change gets logged. If your team still retypes PDFs into a CRM or ERP, it's worth studying how this was put together.

TL;DR#

• Vertafore launched a Policy Intake Agent (documents into system fields) and a Policy Check Agent (issued policies reconciled against records). Neither changes anything until a staff member approves it.
• The useful lesson is the design: extract, compare against the existing record, route the differences to a person, then write and log.
• You can apply the same pattern to invoices, orders, onboarding forms and supplier paperwork in your own stack, without buying a vertical platform.

What Vertafore actually shipped#

The Velocity Policy Intake Agent accepts PDFs, declarations pages, binders, quotes, applications, schedules and spreadsheets, according to ColoradoBiz. It pulls out policy details, maps them to fields in the agency management system, and flags anything new, changed, missing or conflicting. Once a user approves, it updates the record and files the source documents.

The Velocity Policy Check Agent does reconciliation. It compares the policy a carrier issued with the agency's records and source documents, then highlights mismatches in coverage, limits, deductibles and terms. It suggests corrections, applies the approved ones, creates follow-up items where needed and logs the review for audit.

Intake is live in AgencyOne with AMS360 and in early access for Sagitta. Check is available in AMS360. One account manager quoted by FinTech Global put the problem plainly: "Most of our farm policies don't download, which has traditionally meant a lot of manual data entry for our team."

A fair caveat from Beinsure: the 87% and 90% time savings come from early, vendor-reported testing. Treat them as a direction, not a promise.

Why data entry automation keeps disappointing#

Most businesses have tried to automate manual data entry at least once. It usually goes one of three ways.

Template-based OCR works until a supplier changes their invoice layout. Then someone spends a week redrawing extraction zones. Screen-scraping bots (classic RPA) type into the same screens a person would, so a UI update breaks them overnight. And the newer approach, letting a language model read the document and write straight into the system, fails more quietly. It gets 97 fields right and one deductible wrong, and nobody notices until a claim or an invoice dispute.

That last failure mode is the expensive one. A wrong value in your system of record costs more than the time it took to type it. Typing is slow, but a person typing at least looks at the field.

Vertafore's design accepts this. The agent doesn't own the write. It owns the draft. A person sees a short list of proposed changes, not a blank form, and the review takes seconds instead of the whole job taking minutes. You keep human judgment where it matters and drop the keystrokes everywhere else.

The architecture behind safe data entry automation#

Strip away the insurance vocabulary and you get a pipeline you can build around almost any system with an API:

document in
   -> extract fields (with confidence + source page)
   -> validate (types, totals, required fields)
   -> diff against existing record
   -> review queue: new / changed / missing / conflicting
        approved -> write via API -> audit log
        rejected -> feedback -> fix rules or prompts

Where the approaches differ#

The trade-off is honest: the approval step costs some human time on every record. But it's the step that lets you trust the system on day one, and it gives you data on where you can safely loosen control later. If reviewers approve a field without edits 500 times in a row, that field is a candidate for auto-apply.

The reconciliation agent is the underrated half. Comparing two sources of truth (a carrier's policy against your records, Stripe payouts against your ledger, a supplier's invoice against the purchase order) is often where the real money leaks, and it's even more tedious for people than entry.

What this means for businesses outside insurance#

Look for the same shape in your own operations. Any process where someone reads a document and retypes it into a system is a candidate: supplier invoices into accounting, emailed purchase orders into Shopify or Zoho, onboarding forms into a CRM, compliance certificates into a tracking sheet.

You don't need to wait for your software vendor to ship agents. Most data entry automation software on the market today handles the extraction step well. What it rarely does is fit your specific validation rules, your diff logic and the exact place your team already works. That gap is why off-the-shelf tools often end up as one more tab people copy from.

For companies without an internal tech team, the practical question is scope. Start with one document type that has steady volume and a clear destination. Prove the review loop works. Then add the next one. AI data extraction has gotten good enough that the model is rarely the bottleneck anymore; the integration and the review experience are.

How to automate data entry without breaking your records#

1. Pick one document type. Choose high volume, a single destination system and an owner who'll review.
2. Measure the baseline. Time 30 to 50 recent documents and note the error rate. You'll need this to judge results.
3. Write the field schema. Define every field, its type and its rules (dates parse, line items sum to the total, customer IDs exist).
4. Extract with evidence. Store a confidence score and the source page next to each value so reviewers can check in one click.
5. Diff, don't overwrite. Show reviewers only what's new, changed, missing or conflicting.
6. Write through the API. Avoid screen automation. Use idempotency keys so a retry never creates duplicates.
7. Log every decision. Record who approved what and when, and track the no-edit approval rate per field.

How MagicMakers Lab approaches this#

This is the core of our AI Integration & Automation work: we embed extraction and reconciliation into the stack you already run, with scoped tools, human approval gates and decision logging borrowed from our agentic development practice. For Framico, connecting the order flow end to end meant 200+ orders shipped a day with zero manual steps and a support team that went from five people to one. We start with the one workflow costing you the most hours, not a platform rebuild.

Key takeaways#

• Vertafore's two agents automate policy entry and policy checking, but only apply changes after staff approve them.
• The reported 87% and 90% time savings are early, vendor-reported figures, so measure your own baseline.
• The safe pattern is extract, validate, diff, approve, write through the API, then log.
• Reconciliation between two sources of truth is often worth more than entry itself.
• Start with one document type and loosen approvals field by field as the data earns it.

FAQ#

What is data entry automation?#

Data entry automation is software that captures information from documents, emails or files and puts it into business systems like a CRM, ERP or accounting tool without manual typing. Modern versions use AI to read varied layouts, validate the values and propose updates, often with a person approving changes before they're written to the system of record.

Can AI fully automate data entry?#

For some fields, yes, once you have evidence they're reliable. For most businesses, the safer starting point is AI that drafts the entry and a person who approves it. Track how often reviewers approve without edits. Fields with a long clean record can move to auto-apply, while high-stakes values like amounts, limits or tax IDs stay under review.

How do I automate data entry from PDFs into my CRM or ERP?#

Define the fields you need and their validation rules, then use an AI extraction step to turn each PDF into structured data with confidence scores. Compare that data to the existing record, send the differences to a review queue, and write approved changes through the system's API. Logging each approval gives you an audit trail.

Is data entry automation software better than a custom build?#

Off-the-shelf tools are quicker to try and handle extraction well. A custom build makes sense when your validation rules, reconciliation logic or target systems are specific to your business, or when you need everything to happen inside the tools your team already uses. Many companies combine both: a vendor model for extraction, custom code for the rest.

If your team is still retyping documents into your systems, we'll map the workflow with you and show where an approval-based pipeline would save the most time. Book a free audit.

Sources#

• Vertafore launches AI agents to automate insurance policy checks (FinTech Global)
• Denver's Vertafore launches AI tools to automate insurance policy processing (ColoradoBiz)
• Vertafore launches AI agents to automate insurance policy verification and management (Beinsure)