Quick Facts
- Client: Mattsco - Leading US Supply & Logistics Provider
- Industry: Industrial Supply Chain / Hardware Distribution
- Specialization: Pipes, flanges, nuts, bolts, fittings, filters, screws, and industrial hardware
- Vision: Modernize and automate the entire RFQ lifecycle with AI intelligence
- Platform: Custom Web Application (React + Java Spring Boot)
- AI Stack: LLM-driven email parser, Python extraction engine (built in 2023)
- Infrastructure: Microsoft Azure, SendGrid, Azure Function Apps
- Impact: 70% reduction in manual processing time, faster customer quotes, streamlined vendor coordination
- Innovation: Early AI adoption before widespread industry implementation
About the Client
- Client: Mattsco
- Industry: Supply & Logistics
- Specialization: Industrial hardware distribution (pipes, flanges, fittings, filters, fasteners)
- Operations: Fast-paced quote management serving wide customer base
- Challenge: Processing RFQs in countless formats from diverse customer sources
- Vision: Transform fragmented manual workflows into intelligent, automated system
The Challenge
Mattsco's quote management process faced significant operational inefficiencies caused by the complexity of customer RFQ submissions:
Highly Unstructured RFQ Inputs
Customers submitted RFQs in inconsistent formats including:
- Excel spreadsheets with varying structures
- Tabular PDFs with different layouts
- Plain email text without standardization
- Custom templates unique to each customer
This format diversity led to:
- Human errors in manual interpretation
- Delayed vendor communications
- Slow response times to customers
- Inconsistent data quality
Multi-Layer Quotation Workflow Complexity
Mattsco's business model relies on dual sourcing:
- Internal inventory pricing and availability checks
- Third-party vendor outreach for secondary quotes
- Consolidation of multiple pricing sources
- Final customer quote generation with accurate margins
This required coordinating multiple stakeholders and maintaining accuracy across complex pricing logic.
Limited Visibility Across Sales Cycle
- No centralized tracking from customer email to final invoice
- Manual follow-ups with vendors consuming sales team time
- Difficult to monitor RFQ progress and bottlenecks
- Lack of real-time visibility for stakeholders
- Time-consuming quote status reporting
Methodology
- Collaborative design approach with client operational teams
- Agile implementation ensuring real-world usability
- Iterative refinement based on stakeholder feedback
- Close partnership throughout development lifecycle
Phases
- Requirements Analysis: Understanding fragmented RFQ workflows and pain points
- Solution Redesign: Reimagining the existing process with AI capabilities
- AI Parser Development: Building LLM-driven extraction engine (2023)
- Platform Development: React frontend and Java Spring Boot backend implementation
- Integration: Email automation and Azure infrastructure setup
- Testing & Deployment: Quality assurance and production rollout
Key Innovation Decision
Early AI Adoption (2023): Edstem implemented advanced LLM-driven parsing capabilities when AI adoption in RFQ processing was still emerging in the industry. This pioneering approach gave Mattsco a significant competitive advantage before AI became widespread in procurement workflows.
Solution
Intelligent AI-Based Email Parser
- LLM-driven parser capable of reading any RFQ email format
- Automatic conversion of unstructured data into structured model
- Intelligent identification of item names, quantities, and units
- Automatic mapping to Mattsco's internal product database
- Data normalization into unified structure
- Drastically reduced manual effort and improved accuracy
- Built with Python extraction engine
End-to-End RFQ Workflow Automation
Sales Team Management:
- RFQ assignment and tracking dashboard
- Real-time progress visibility
- Automated status notifications
Vendor Communication Automation:
- Integrated email services via SendGrid
- Automated vendor outreach for secondary quotes
- Systematic vendor response collection
- Follow-up automation reducing manual effort
Quote Consolidation:
- Multi-source pricing aggregation
- Internal inventory and vendor quote comparison
- Final customer quote generation
- Invoice creation automation
Complete Lifecycle Visibility:
- Real-time updates for all stakeholders
- End-to-end RFQ tracking from email to invoice
- Centralized dashboard for sales team
Modern Technical Architecture
Frontend Layer:
- React for responsive, modern user interface
- Real-time updates and interactive dashboards
Backend Layer:
- Java Spring Boot for robust business logic
- RESTful API architecture
- Scalable microservices design
AI Extraction Engine:
- Python-based LLM integration
- Advanced natural language processing
- Intelligent data extraction and mapping
Cloud Infrastructure:
- Microsoft Azure for reliability and performance
- Azure Function Apps for serverless integrations
- Secure, scalable deployment
Email Automation:
- SendGrid integration for vendor communications
- Automated email workflows
- Template management system
Technical Stack Summary
| Layer | Technology |
|---|---|
| Frontend | React |
| Backend | Java Spring Boot |
| AI Extraction Engine | Python |
| Cloud Infrastructure | Azure |
| Email Automation | SendGrid |
| Serverless Integrations | Azure Function Apps |
Impact
Operational Efficiency Transformation
- 70% Reduction in Manual RFQ Processing Time
AI parser eliminated need for manual data entry from emails
Automated workflows removed repetitive tasks
Sales team focus shifted to customer relationships
Enhanced Customer Service
- Faster & More Accurate Customer Quotes
Unified data structure improved consistency
Automated workflows reduced turnaround time
Error rates significantly decreased
Customer satisfaction improved through faster response
Streamlined Vendor Coordination
- Automated email outreach improved vendor response rates
- Systematic tracking minimized follow-up effort
- Better vendor relationship management
- Reduced time spent on vendor communications
Centralized Sales Visibility
- End-to-end visibility across all RFQ stages
- Real-time tracking from customer email to invoice
- Better forecasting and pipeline management
- Improved coordination across sales team
Competitive Advantage Through Early AI Adoption
- Industry Leadership Position
Implemented AI capabilities in 2023, ahead of widespread adoption
Gained competitive edge in quote processing speed
Positioned as technology innovator in industrial supply sector
Complete Platform Ownership
- Full code ownership and transparency delivered to Mattsco
- No vendor lock-in or dependency concerns
- Flexibility for future enhancements
- Complete control over platform evolution
Technical Highlights
- Pioneering AI Parser: Built advanced LLM-driven extraction engine in 2023, before widespread AI adoption
- Format Flexibility: Handles any RFQ format—Excel, PDF, plain text, custom templates
- Intelligent Mapping: Automatic product database matching and normalization
- End-to-End Automation: Complete workflow from email receipt to invoice generation
- Scalable Architecture: Azure-based infrastructure supports business growth
- Integration Excellence: Seamless SendGrid email automation for vendor communications
- Real-Time Visibility: Complete stakeholder transparency across RFQ lifecycle
Why Mattsco Chose Edstem
Expertise in AI and Custom Software Engineering
Edstem's team built an advanced AI parser when AI-driven RFQ processing was still a new concept in the industry, demonstrating forward-thinking technical capability.
Proven Experience With Enterprise Technologies
Deep expertise in Azure cloud services, complex integrations, and enterprise-grade architecture provided confidence in scalable solution delivery.
Ability to Deliver Full Ownership of Code
Mattsco received complete control and transparency over the delivered platform, ensuring long-term flexibility and independence.
Collaborative and Agile Implementation
Close partnership with Mattsco's operational team throughout development ensured real-world usability and practical workflow optimization.
Future Vision
The platform is architected for continued evolution and enhancement:
- Advanced AI Capabilities: Integration of newer LLM models for improved accuracy
- Predictive Analytics: AI-driven insights on pricing trends and vendor performance
- Mobile Access: Native mobile apps for on-the-go RFQ management
- Expanded Integrations: Direct ERP and accounting system connections
- Machine Learning Optimization: Continuous improvement of product mapping accuracy
- Customer Portal: Self-service RFQ submission and tracking for customers
- Analytics Dashboard: Business intelligence and performance reporting
Conclusion
Through Edstem's AI-driven RFQ processing platform, Mattsco transformed an error-prone, manual quoting process into a fully automated, intelligent workflow. The solution not only improved operational efficiency by 70% but also empowered Mattsco to deliver faster, more reliable service to its customers. By implementing advanced AI capabilities in 2023—ahead of widespread industry adoption—Mattsco gained a significant competitive advantage in the industrial supply chain market. Edstem continues to partner with organizations across the globe to build AI-powered digital solutions that modernize operations and accelerate business growth.
