Case study

How a workforce services firm achieved 40% faster approvals with CloudFiles' line item extraction for employee verification

CloudFiles enabled fast, accurate processing of employee verification documents by automating the extraction of key fields and line-item salary data directly into Salesforce.

Illustration for the workforce services firm case study

Overview

A workforce services firm responsible for handling large volumes of employee verification requests transformed their review process using CloudFiles' Document AI. By extracting employee PII, payment information, and long salary tables as structured line items, the team accelerated approvals, eliminated manual errors, and unlocked new reporting capabilities.

Business challenges

  • Unstructured employee verification docs – information like employee ID, certifications, and payment history was scattered across multiple formats, making consistent extraction difficult.
  • Multiple data points for verification – each request required accurate capture of PII and financial data for decision-making; manual entry delayed approvals and increased error rates.
  • Large, multi-page salary tables – salary records spanned months or years across multi-page tables and were historically ignored due to extraction complexity.

Solution

  • Automated file processing on upload – documents uploaded via email-to-case triggered immediate AI processing, eliminating manual delays.
  • Contextual data extraction into Salesforce fields – AI parsed unstructured documents and filled key fields like employee details, payment data, and certifications.
  • Accurate table-to-line item extraction – AI extracted multi-column salary tables into line items under the main Salesforce record, enabling detailed reporting.
  • New reporting capabilities – line item data powered new dashboards and analytics previously unavailable.

Business challenges, in depth

Handling diverse document formats, extracting critical information quickly, and making use of large salary tables required a more scalable and intelligent approach to keep up with growing verification volumes.

Handling inconsistent document formats
"We'd receive payment verification files in all shapes and sizes – spreadsheets, scanned PDFs, structured forms. Finding the right data was always a manual hunt." Key verification data was often labeled differently or placed inconsistently across pages.
Fast, accurate extraction of critical data
"To verify employment and approve requests, we had to extract PII and salary details fast – but accuracy was critical." Manual extraction often resulted in delays or rework due to missing or incorrectly mapped information.
Unlocking value from large salary tables
"Some files had salary data spanning years, but extracting those massive tables was just too time-consuming." Lack of structured salary data limited the ability to build reports or identify trends across employee cases.

How CloudFiles solved it

CloudFiles introduced a fully automated document workflow, instantly processing uploaded files, extracting key data fields, and converting large salary tables into structured line items.

Trigger-based AI processing
Once uploaded to Salesforce, files were immediately sent to CloudFiles for AI processing, ensuring zero wait time and no changes to the team's existing upload process.
Field-level extraction with contextual prompts
CloudFiles used specialized prompts tailored to each employer's format and document type to extract employee PII (name, ID, certifications) and key employment and salary details, ready for review and approval.
Table-to-line item conversion
Large tables were no longer ignored: CloudFiles extracted relevant columns like date, amount, and pay type, created structured line items in Salesforce tied to each case, and enabled management to run new reports and dashboards on the detailed data.

Rollout & secondary impact

The rollout was quick, seamless, and designed to fit right into existing workflows, delivering value from day one.

Week 1-2: development

Deployed AI for basic field extraction and Salesforce mapping, running alongside the current workflow so users could compare AI outputs with manual inputs without interrupting operations.

Week 3-4: testing & tuning

Introduced advanced prompt refinement to handle varied document formats, using real document samples to fine-tune accuracy and contextual understanding.

Instant adoption

Targeted training sessions ensured the team could confidently validate and override AI-suggested matches when needed, leading to fast adoption across the team.

Impact & results

Integrating CloudFiles' Document AI into the verification workflow achieved faster turnaround, deeper reporting capabilities, and higher data quality.

40% faster approval times
Manual processing previously took 2-3 hours per request; after CloudFiles, AI delivered review-ready data in under 10 minutes. Across 300 monthly requests, that's over 300 hours saved per month – a 40% efficiency gain.
Actionable insights from previously unused data
Salary tables, once too large to process, were converted into structured line items, enabling dashboards that track payment trends, employee tenure patterns, and bottlenecks.
Higher accuracy, productivity & confidence
AI ensured consistent field mapping and flagged inconsistencies before approval, so teams spent less time on data entry and more time on strategic reviews.

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