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Case StudyInsuranceData Entry

From Minutes to Seconds: Data Entry in Insurance

See how a insurance team automated data entry with AI — processing time per record from minutes to seconds. Read the playbook →

Processing Time per RecordMinutesSeconds

The Impact

Before → after across the metrics that matter for insurance data entry.

Processing Time per Record

MinutesSeconds

Dramatically faster

Error Rate

NoticeableMinimal

Major reduction

Data Availability Lag

DaysSame day

Near real-time

Annual Labor Cost

HighFraction of manual cost

Major savings

Processing Capacity

LimitedDramatically higher

Massive throughput increase

Company

Insurtech company

Team Size

15-80 employees

Industry

Insurance

Setup Time

Half a day

Agents

4 AI agents

The Challenge

This insurtech company was trapped in a data entry nightmare. Every day, their team of 15-80 employees received insurance-specific documents in dozens of formats — PDFs, scanned images, spreadsheets, emails, and handwritten forms. Each document required manual extraction and entry into multiple systems, with the average record taking 8-12 minutes to process completely.

The cost was staggering. Between direct labor ($85K+ annually in data entry staffing), error correction costs, and the opportunity cost of delayed data availability, the organization estimated they were spending over $150K per year on what was essentially a solved problem. Worse, the manual process created a 48-hour lag between document receipt and data availability, meaning their insurance team was always working with outdated information. Critical decisions were being made based on data that was days old.

The Solution

DeskFerry gave this insurance team the data entry automation they needed. The implementation connected their existing tools — Applied Epic, Gmail, and Zapier — and deployed AI agents that could understand, extract, and validate data from any insurance document type they received.

The key innovation was the validation layer. Rather than just extracting data and hoping for the best, the AI agents cross-reference every extracted field against insurance-specific business rules, historical patterns, and related records in the system. Duplicate detection catches records that already exist, format validation ensures data consistency, and anomaly detection flags values that fall outside expected insurance ranges. The result is data that enters their systems clean, accurate, and ready for use — without any human touching a keyboard.

Tools Connected

Applied EpicSalesforceDocuSignGmailGoogle Sheets

How They Did It

From zero to production in Half a day — no code required.

Step 1: Mapped the existing data entry workflow

Documented every step of the current manual data entry process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.

Step 2: Built the automation in DeskFerry

Used DeskFerry's no-code builder to create the data entry workflow: connected Applied Epic and DocuSign as data sources, configured AI decision logic for insurance-specific requirements, and set up automated actions and notifications.

Step 3: Parallel run with manual process

Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on the vast majority of data entry instances, with edge cases automatically flagged for human review.

Setup Time

Half a day

AI Agents

4 AI agents

Tools Connected

5 integrations

What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our insurance data entry workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.

Director of Business Operations

Insurtech company

Key Takeaways

The most important lessons from this insurance data entry project.

This insurance team proved that data entry automation doesn't require technical expertise — the no-code platform made it accessible to business users.

Scaling data entry capacity dramatically without adding headcount fundamentally changed the economics of their insurance operations.

Consistent AI-powered processing eliminated the quality variance that came with different team members handling data entry differently.

Real-time visibility into data entry metrics gave leadership the data they needed to make better strategic decisions.

Frequently Asked Questions

Common questions about automating data entry in insurance.

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This case study represents a typical customer scenario. Individual results may vary.