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Online: 3K+ Agents Active
Digital Worker 10 AI Agents Active

Intelligent RFQ-to-Quote Processing System

An orchestrated multi-agent AI system automates the entire RFQ-to-quote lifecycle in minutes. Agents work in parallel to analyze specifications, check inventory, calculate optimal pricing, validate compliance, assess supply chain risks, and generate strategic recommendations with full audit trails.

10 AI Agents
8 Tech Stack
AI Orchestrated
24/7 Available
Worker ID: RFQQuoteProcessor

Problem Statement

The challenge addressed

Traditional RFQ processing takes 5-7 business days with manual workflows, disconnected data sources, and limited visibility into pricing optimization, compliance verification, and supply chain risks. This delays customer response times and increases...

Solution Architecture

AI orchestration approach

An orchestrated multi-agent AI system automates the entire RFQ-to-quote lifecycle in minutes. Agents work in parallel to analyze specifications, check inventory, calculate optimal pricing, validate compliance, assess supply chain risks, and generate...
Interface Preview 4 screenshots

RFQ Processing Dashboard - Overview of multi-agent RFQ workflow and processing status

Quote Generation - AI-powered pricing analysis and inventory allocation

Compliance Validation - Certification verification and audit trail management

Supply Chain Risk Assessment - Risk scoring and mitigation recommendations

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

10 Agents
Parallel Execution
AI Agent

Workflow Coordinator

Complex multi-agent workflows require coordination to manage dependencies, handle failures, and ensure all processing steps complete successfully with proper handoffs between specialized agents.

Core Logic

Coordinates all agents through a 14-step workflow, managing task routing, agent delegation, and workflow execution. Monitors processing state, handles retries, and ensures end-to-end completion with consolidated audit trails and quality assurance checkpoints.

ACTIVE #1
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AI Agent

Technical Specification Expert

Customer RFQ line items contain free-text descriptions, varied terminology, and incomplete specifications that must be matched to thousands of catalog products accurately.

Core Logic

Uses RAG-enabled semantic search to parse technical specifications from line item descriptions, match them to the product catalog with confidence scoring, identify material types, standards compliance, and recommend alternatives when exact matches are unavailable.

ACTIVE #2
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AI Agent

Inventory Intelligence

Determining product availability requires querying multiple warehouse locations, accounting for reservations, and calculating optimal fulfillment strategies across distribution centers.

Core Logic

Queries real-time inventory levels across all distribution centers (Dallas, Chicago, LA, Atlanta, Seattle, Phoenix), calculates stock availability against requested quantities, allocates stock optimally, and determines lead times for make-to-order items.

ACTIVE #3
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AI Agent

Dynamic Pricing Expert

Optimal pricing requires balancing competitive positioning, volume discounts, margin requirements, and customer-specific agreements while ensuring profitability thresholds.

Core Logic

Calculates unit and extended pricing with automated volume discount tiers, optimizes margins within defined thresholds, compares against competitor pricing, and escalates high-value or low-margin line items for human approval.

ACTIVE #4
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AI Agent

Regulatory & Quality Expert

Industrial customers require verification of RoHS, REACH, ISO 9001, and industry-specific certifications, with documentation readily available and expiration tracking.

Core Logic

Validates requested certifications against product compliance database, checks certification validity and expiration dates, generates compliance status reports, and flags any gaps requiring customer communication.

ACTIVE #5
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AI Agent

Strategic Advisor

Customers benefit from cost-saving alternatives, faster delivery options, and strategic insights, but manual analysis of these opportunities is time-prohibitive.

Core Logic

Analyzes quote data to identify cost-saving alternatives (material substitutions, standard vs. custom parts), faster delivery options, volume consolidation opportunities, and generates prioritized recommendations.

ACTIVE #6
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AI Agent

Risk Sentinel

Supply chain disruptions from supplier issues, geopolitical events, logistics delays, or quality problems can impact order fulfillment but are difficult to predict and quantify.

Core Logic

Assesses supplier reliability scores, monitors geopolitical risk factors, evaluates logistics conditions, calculates overall supply chain risk scores, identifies single-source dependencies, and recommends mitigation strategies like buffer stock or alternative suppliers.

ACTIVE #7
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AI Agent

Eco Advisor

ESG requirements and sustainability reporting demand carbon footprint calculations and eco-friendly product alternatives, which require specialized analysis across material lifecycle.

Core Logic

Calculates total CO2 emissions across manufacturing, raw materials, transportation, and packaging. Identifies eco-friendly alternatives with recycled content, quantifies sustainability improvements, and provides offset cost estimates for carbon neutrality.

ACTIVE #8
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AI Agent

Market Oracle

Material costs fluctuate with commodity markets, and competitive pricing requires awareness of market trends, competitor positioning, and demand forecasts.

Core Logic

Monitors real-time commodity prices (steel, stainless, zinc, nickel), tracks price volatility indices, gathers competitor pricing intelligence, analyzes market trends, and forecasts demand patterns to inform pricing strategy.

ACTIVE #9
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AI Agent

Foresight Engine

Quoted lead times often miss actual delivery performance, and demand fluctuations require predictive capabilities beyond static estimates.

Core Logic

Applies ML models to predict accurate lead times based on historical supplier performance, current capacity utilization, and external factors. Generates demand forecasts with seasonality detection and provides confidence intervals for predictions.

ACTIVE #10
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The RFQ-to-Quote Processing Digital Worker orchestrates 10 specialized AI agents through a 14-step workflow to transform customer RFQ submissions into comprehensive, optimized quotes with supply chain insights, sustainability metrics, and predictive analytics. The system processes multi-line RFQs, performs semantic product matching via RAG, runs real-time inventory allocation, applies dynamic pricing strategies, and generates executive-ready quote packages with full traceability.

Tech Stack

8 technologies

Integration with product catalog and RAG-enabled semantic search

Real-time inventory API connectivity across warehouse locations

Pricing engine with volume discount and margin optimization rules

Compliance database with RoHS, REACH, ISO certification records

Supply chain risk monitoring with geopolitical and supplier analytics

Sustainability metrics engine with carbon footprint calculation

Market intelligence feeds for commodity pricing and competitor data

ML-based lead time prediction models with historical performance data

Architecture Diagram

System flow visualization

Intelligent RFQ-to-Quote Processing System Architecture
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