Case Study

AI-Powered RFP Response Generation

A Leading Global Audit Company Automates and Accelerates its RFP Response Generation Process

A leading global audit company automated and accelerated its RFP response generation process with an end-to-end AI/NLP (Natural Language Processing) Solution and reduced manual effort.
Industry & Region: Audit and Assurance, US
Technology Stack: Python, NumPy, Databricks, Pandas, React, TensorFlow, Keras, Scikit Learn, Azure
Client Overview
The client is a leading audit services provider with an immense global presence. They provide a wide range of audit and assurance services to their clients from a wide range of industries.
Business Challenge
Our client was facing challenges with their existing process of creating responses for RFPs submitted by their prospects, which was heavily labor-intensive and time-consuming. The SME (Subject Matter Experts) team was investing a significant amount of their time in the process of generating these RFP responses manually – performing tasks like gathering information, drafting the proposals, and ensuring regulatory compliance as per customer business requirements. As the volume of RFPs handled by our client is extremely high, this process reduced the efficiency and productivity of the SME team.
Solution Offered
Our team of experts at KANINI recognized the need to streamline and automate the RFP response generation process for our client. We proposed an NLP-powered solution that would extract and analyze information from large volumes of data and generate draft responses for RFPs, by recommending suggestions. This enabled the SME team to utilize their time effectively in validating the RFP responses generated by the solution and be ready with the response in a shorter timeframe.
Value Delivered
  • Enabled quicker RFP response creation.
  • Reduced effort and increased efficiency in the RFP response generation process.
  • Empowered SMEs to focus on the validation of automatically generated RFP responses than mundane manual tasks.
  • Improved Correctness, Completeness, and Consistency for RFP response assets across the enterprise.
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