Automation Workflow for B2B Industrial Product Catalog and RFQ Package Generation

The workflow begins by ingesting raw product data from ERP (e.g., SAP, Oracle), PLM, and CAD systems. LLM and rule-based agents parse technical specifications, 3D model metadata, and component BOMs to create a unified, queryable product master. This eliminates the manual reconciliation of fragmented data sources, which typically consumes 15-20 hours per new product introduction.

A primary orchestration agent receives a trigger from the CRM (e.g., Salesforce) containing the target account's industry, past purchases, and compliance requirements. Using LangGraph or a custom orchestrator, it queries the normalized product master, filters and prioritizes relevant items, and generates a tailored catalog structure. It personalizes descriptions, highlights applicable certifications, and adjusts technical language for the buyer's persona.

This component integrates with CPQ logic to generate price sheets, commercial terms, and delivery schedules. When a catalog is requested, the agent pulls approved pricing matrices, calculates customer-specific discounts, and assembles a complete quotation PDF. It dynamically attaches relevant CAD files, spec sheets, and compliance documents from the DAM, creating a turnkey proposal package ready for sales review or direct dispatch.

Before any customer-facing document is released, the workflow routes outputs through configurable approval gates. Sales engineers review technical accuracy, while legal/compliance agents screen for regulated claims. The system uses a centralized task queue (integrated with Jira or ServiceNow) to manage assignments, escalate bottlenecks, and log all changes with a full audit trail, ensuring governance without creating friction.

Once approved, the final catalog and RFQ package are dispatched via the customer's preferred channel—email, secure portal, or embedded directly in a CRM opportunity. The workflow automatically updates the CRM record with the generated documents, notes the interaction, and can set follow-up tasks for the account executive. This closes the loop between marketing automation and sales execution, providing full visibility into the buyer's journey.

The entire workflow is instrumented for observability. Dashboards track cycle times, approval bottlenecks, and most-accessed catalog sections. Feedback on quote win/loss rates is fed back to the catalog assembly and pricing agents, allowing for continuous tuning of product recommendations and commercial terms. This data-driven layer turns a static automation into a learning system that improves win rates and margin over time.

The workflow's reliability depends on a clean, structured master product database. This requires integrating and normalizing data from fragmented sources: ERP (SAP, Oracle), PLM, supplier portals, and legacy spreadsheets. An upstream data pipeline with LLM-powered extraction and validation agents must reconcile part numbers, technical specs, and pricing tiers before feeding the generative layer. Poor data quality here directly translates to inaccurate quotes and catalog errors.

The core logic uses specialized, orchestrated agents (LangGraph, CrewAI) for distinct tasks: a Specification Agent pulls customer-specific requirements from CRM (Salesforce) or CPQ; a Content Agent generates tailored product descriptions and application notes; a Pricing Agent applies negotiated contracts and volume breaks; and a Document Assembly Agent collates outputs into a formatted RFQ package with integrated CAD files. This decomposition allows for precise control, auditing, and human-in-the-loop review at each step.

The workflow must be triggered by and feed back into operational systems. Key integrations include: listening for "Create Quote" events in CRM/CPQ, pulling live inventory and lead times from ERP, and pushing final quote documents back to CRM as attachments. The architecture should use event-driven messaging (e.g., Kafka, webhooks) and API gateways to ensure resilience. Rollout often starts with a pilot sales region before enterprise-wide deployment to manage change.

Fully autonomous quote generation is rare in complex B2B sales. The workflow must include configurable approval gates: for example, any discount over 15% or non-standard terms routes to a sales manager. Every AI-generated output, data source used, and decision rationale must be logged to an immutable audit trail. This is critical for compliance, debugging errors, and building trust with the sales team that their margins and relationships are protected.

The primary ROI comes from accelerating the sales cycle and improving win rates. By generating a customer-specific, technically detailed catalog and quote package in minutes instead of days, sales engineers can respond to RFPs within the critical first 24-hour window. This directly increases quote throughput, improves the quality of proposals (reducing errors), and allows the sales team to focus on relationship-building and negotiation rather than administrative assembly work.

A successful build follows a phased rollout: Phase 1 automates the generation of standard catalog pages for top product families. Phase 2 introduces basic RFQ templating with fixed pricing. Phase 3 layers in full personalization, CRM integration, and approval workflows. Parallel to technical build, a change management program for the sales organization is essential—demonstrating the tool as a leverage multiplier, not a replacement, and incorporating their feedback into guardrail design.