Impact at a Glance
By partnering with Cognida.ai, a global manufacturer achieved:
- 80% accuracy in price prediction – Automated data extraction minimized errors, ensuring accurate, reliable quotes.
- 70% reduction in quote turnaround time – The client responded to RFQs faster, leading to higher conversion rates
- 25% improvement in customer satisfaction – Faster response times and accurate quotes improved customer experience.
About the client
A leading manufacturer provides belting solutions to clients across multiple industries like food processing, packaging and textiles. The company is an established player in the market with over 70 years of experience offering consistent quality and customer experience.
Business Challenge: Addressing Pricing and Communication Inefficiencies
The client received a huge volume of RFQ (Request for Quote) emails, each including unique specifications, timelines, quantities, and pricing requests. The client’s team had to review each RFQ manually, extract key information and generate a custom quote. This led to delays, errors and inefficiencies that would ultimately affect customer satisfaction levels and lead to the risk of lost business opportunity.
- Reviewing each RFQ email was tedious and resulted in slow, inconsistent response times.
- Manual data extraction of key information increased the risk of inaccurate quotes.
- Different interpretations of RFQs impacted pricing strategies.
Cognida.ai Solution: AI-Powered Quote Generator
After a thorough analysis of client’s ERP systems, Cognida.ai developed an AI-powered Quote Generator to automatically read, understand and extract key information from the client’s RFQ emails.
Extracting key product information, from unstructured data, such as ‘product code’, ‘quantity’ and ‘specification’ helped classify messages based on intent.
The AI model validated the extracted data and generated a quote based on the client’s pricing system. Integrating with the client’s existing ERP and CRM systems, the solution helped to automatically input quote data, track RFQs and follow-up with customers.
The AI model was trained on standardized rules and algorithms, ensuring consistent and competitive pricing. A continuous feedback loop improved the model’s accuracy in data extraction and quoting precision, thereby streamlining quote generation activities to save time, cost, and resources.
