Our client is a global audit and assurance company enabling businesses to modernize their processes for improved business outcomes by leveraging advanced technology-powered solutions.
The audit and advisory firm was expanding its global footprint and it had received multiple RFPs (Requests for Proposal) for various projects. Their current process of responding to the RFPs was largely manual. The SMEs (Subject Matter Experts) had been manually gathering information to draft the responses as per the different RFP guidelines. This process was both time-consuming and labor-intensive, and it was causing delays in the RFP response generation times. As the business grew, the company needed to streamline its RFP response generation process and achieve faster turnaround.
To empower the SMEs to accelerate their RFP responses, KANINI proposed an end-to-end NLP solution that would automate and streamline the RFP response creation process. Leveraging our Databricks’ expertise, we built an automated RFP response generator on Databricks and integrated it with a range of advanced analytics tools, machine learning algorithms, and NLP techniques to address our client’s pain points and requirements. Databricks is a powerful data processing and analysis platform, and it brought in fast and seamless ingestion of large volumes of RFP documents. Also, Databricks supports the integration of a wide range of NLP libraries and frameworks, and this NLP capability allowed the solution to understand the RFP guidelines, automatically extract relevant information, and analyze the data to deliver intelligent recommendations on drafting more accurate and tailored RFP responses in line with the guidelines.
Databricks’ workflow automation capabilities alleviated the efforts of the SME team in drafting the RFP responses.
Improved process efficiencies resulted in faster responses to RFPs.
Intelligent recommendations and the collaborative environment gave SMEs the opportunity to validate and produce superior RFP response documents to ensure correctness, completeness, consistency, and compliance.
The elasticity and scalability of Databricks allowed the organization to scale up the solution based on workloads.
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