Digital RFQ Response Automation

A multinational engineering and manufacturing company specializing in materials services, automotive technology, and industrial solutions partnered with an automation consultancy in 2025 to deploy AI-powered RFQ scoring and evaluation. According to the Roboyo case study published in 2025, the manufacturer had previously relied on manual extraction of data from dense, unstructured technical documents spanning multiple languages and formats, resulting in inconsistent scoring logic across departments and regions. The implemented solution leveraged generative AI through Microsoft Azure OpenAI, combining retrieval-augmented generation with natural language processing to automate RFQ scoring, risk evaluation, and multilingual document understanding. The system delivered a reported revenue uplift attributed to faster and more consistent sourcing decisions, along with approximately 570,000 euros in direct cost savings from reduced manual processing.

In a separate implementation profiled by McKinsey in 2024, a European industrial equipment manufacturer deployed a dedicated configure-price-quote system to automate quote creation and free frontline sales teams from back-office dependencies. The company conducted a bottom-up review of configuration protocols to ensure sufficient standardization, then implemented automated template generation with correct conditions and legal terms. According to the McKinsey case study, the manufacturer cut quotation times by approximately 50% while simultaneously boosting cross-selling activities. Sales teams reported improved adoption because the system reduced administrative burden and enabled a guided selling approach focused on customer goals rather than manual data assembly.