AI Workflow
Examples

Problem

Sales engineers manually open RFQ emails, review attachments, search previous quotations, coordinate with engineering, and draft responses. The work is slow, fragmented across systems, and easy to bottleneck under volume.

Business Context

Reference implementation of an AI workflow layer for B2B companies handling customer RFQs — commonly discrete manufacturers — across existing email, document, quotation, and approval systems, without replacing ERP, CRM, CAD, or PLM.

AI System

An AI-assisted RFQ workflow implementation that monitors incoming RFQs from existing email workflows, extracts requirements from PDFs, drawings, and spreadsheets, retrieves similar historical quotations, generates AI-assisted recommendations, and routes Microsoft Teams approval before drafting a customer reply.

Deployment Notes

Workflow integration across Outlook / email, document stores, historical quote knowledge, Microsoft Teams approval, and existing ERP / CRM / CAD / PLM context. Human engineer confirmation remains required before any customer-facing response. Each deployment is customized to the customer's systems, data, and workflow requirements.

Problem

Operations teams manually triage requests, search knowledge sources, apply business rules, seek approval for exceptions, and update CRM or ERP records. The work is repetitive and easy to delay when volume rises.

Business Context

Example AI workflow for operations teams where incoming requests must be triaged, researched, decided, and recorded across business systems.

AI System

An AI workflow that classifies input, retrieves approved knowledge, applies business logic, and prepares an action for CRM or ERP handoff. Human approval remains required for exceptions.

Deployment Notes

Connects to existing record systems through controlled handoffs. Automated writes are prepared for review where policy requires human control. Each deployment is customized based on the customer's systems, data, and workflow requirements.

Problem

Employees waste time searching policies, catalogs, and internal documents for recurring answers. Versions conflict, ownership is unclear, and people reconstruct the same response repeatedly.

Business Context

Example AI knowledge workflow for teams that rely on internal policies, operational documents, and commercial guidance spread across shared drives and knowledge repositories.

AI System

An AI knowledge workflow that indexes approved documents, retrieves relevant sources, and returns a referenced response for internal questions. Source attribution and escalation for low-confidence answers stay in place.

Deployment Notes

Connects document sources to retrieval with access-aware handling and source traceability. It does not replace the underlying document systems. Each deployment is customized based on the customer's systems, data, and workflow requirements.