Case Study · Cross-industry
Smart DTO Analyzer
Analysing structured data and documents to surface discrepancies and prepare review-ready summaries.
- Industry
- Cross-industry
- Category
- Document Intelligence
- Capabilities
- Document Intelligence, Decision Support
The problem
Reviewers manually compared large data sets and supporting documents to find inconsistencies, a slow process prone to missed items.
Existing manual process
Reviewers compared exported data sets with supporting documents line by line in spreadsheets.
Agent / workflow deployed
An agent that ingests structured data and documents, cross-checks them, highlights discrepancies and prepares a summary for review.
Systems touched
The workflow
What the agent does, step by step.
- 01
Data and document intake
- 02
Field extraction
- 03
Cross-checking
- 04
Discrepancy detection
- 05
Summary preparation
Reviewers confirm findings and decide on action.
Operating model
Before and after.
Before
Line-by-line manual comparison, with sampling when volumes were high.
After
Agent cross-checks every record and prepares a discrepancy summary for reviewers.
Measured results
Verified before published.
No metrics are published for this case yet. Once the customer confirms the measured change against their baseline (turnaround, cost per transaction, accuracy), we will add it here.
Related
Keep exploring.
More case studies
Related work.
Cross-industry
Smart RFP AI
Cross-industry
SmartComm Analyzer
Banking
Banking KYC Intelligence
Have similar work?
We can walk you through how this approach would apply to your workflow.