Search

Q

CASE STUDY

Document Processing and RFP Assistant for Enterprise Workflows

INDUSTRY

Retail & Consumer

USE CASES

Document Processing & Extraction Knowledge Extraction RFP Automation

BUSINESS IMPACT

Efficiency Improvement Time-to-Market Acceleration

TECHNOLOGIES

AWS Azure Document parsing and extraction Generative AI LLM-based Systems Natural Language Processing (NLP) Python Retrieval-Augmented Generation (RAG) Vector databases

Client Overview

Industry

Retail & Consumer

Region

Europe

Company Size

Mid-market to Enterprise

Profile

Organization handling large volumes of documents, including RFPs, contracts, and structured/unstructured business materials

Project Background

The client required a more efficient way to process documents and respond to complex information requests.

The challenge

Document-heavy workflows relied on manual review and fragmented processes.

This resulted in:

  • Significant time spent reviewing and extracting information from documents
  • Inconsistent responses across teams
  • Difficulty scaling RFP responses and document handling
  • Limited ability to reuse knowledge across documents

There was a need for a system that could extract, structure, and generate responses based on document content.

What we built

We designed and deployed a document intelligence system combined with an AI assistant to support RFP and document workflows.

The solution included:

  • Automated extraction of structured data from unstructured documents
  • A retrieval-based system to access relevant content across document sets
  • A generative AI layer to assist in drafting responses
  • Integration with internal workflows to support document review and response processes

The system was designed to reduce manual effort while maintaining accuracy and consistency.

Impact in practice

The implementation enabled a more data-driven and responsive production environment:

Reduced time spent on document review and extraction

by automating key parts of the workflow

More consistent and structured outputs

through standardized data extraction and response generation

Faster turnaround for RFP and document-based workflows

by enabling quicker access to relevant information

Improved reuse of knowledge across documents

through centralized retrieval and structured data

Technologies used

Several technologies were used in order to deliver this project for the client, among which:

Document parsing and extraction
Generative AI
LLM-based Systems
Natural Language Processing (NLP)
Python
Retrieval-Augmented Generation (RAG)
Vector databases

Continue reading

Related Case Studies

The next chapter starts here

Ready to create  your Success Story?