Tables in Staple
Client
StapleAI
Year
2024
Transforming Enterprise Document Processing- From Manual Data Correction Chaos to AI-Powered Interactive Mapping Workflows
● The Challenge
Usage analytics from 150+ daily users processing 10,000+ documents monthly
Task observation sessions documenting current extraction workflows and failure points
Competitive research evaluating Google Vision, Nanonets, and 8 enterprise alternatives
97% accuracy achievable when AI understands header structure and business context
Users abandon complex extraction tasks after 3 failed attempts (73% abandonment rate)
Visual feedback and real-time guidance reduces user uncertainty by 60%
● Discovery & Research
Research Methods
Usage analytics from 150+ daily users processing 10,000+ documents monthly
Task observation sessions documenting current extraction workflows and failure points
Competitive research evaluating Google Vision, Nanonets, and 8 enterprise alternatives
97% accuracy achievable when AI understands header structure and business context
Users abandon complex extraction tasks after 3 failed attempts (73% abandonment rate)
Visual feedback and real-time guidance reduces user uncertainty by 60%
● Core Features Deep Dive
Feature 1: Optimising the extracted fields area
I led the design process, employing an iterative approach that emphasized user feedback and technical collaboration
Involvement of colours in the extracted data: Colours were chosen such that it can be differentiated by colour blind people also.
This involved having competitive research were I checked the competitor products like Google vision, Nanonets etc
● Core Features Deep Dive
Feature 2: Optimising the extracted table area
I aimed to transform the table structure, ensuring exceptional clarity and intuitive organization, empowering users to effortlessly interpret and utilize extracted data.
● Core Features Deep Dive
Feature 3: Interactive AI-Powered Table Extraction
The core innovation transforms table extraction from a black-box process into an interactive, visual workflow where enterprise users maintain control while benefiting from AI assistance.
Enterprise Workflow Steps:
AI Visual Table Detection: Intelligent identification and highlighting of table boundaries with confidence scoring -
Interactive Header Selection Mode: Users select and map column headers directly with business context awareness
Smart Field Mapping: Drag-and-drop assignment of data fields with AIpowered suggestions and validation
Real-Time Population Preview: Instant preview of extracted line items with accuracy indicators
● Impact & Results
50% reduction in time spent correcting extraction errors across enterprise workflows
30-40% improvement in overall extraction accuracy reducing downstream operational costs
3x faster document processing workflows enabling accelerated business decision-making
73% reduction in IT support tickets related to table extraction and document processing issues
● User Testimonials
"The new table extraction feels like magic for our finance team. What used to take our analysts 20 minutes per invoice now takes 3 minutes with higher accuracy"
"Finally, a system that understands our complex operational reports. The visual mapping is intuitive enough for our non-technical managers while maintaining the accuracy"
