The Problem Statement

The client, tasked with producing forward-looking demographic analytics for the next decade, faced a monumental challenge in managing and processing massive volumes of heterogeneous data. The problem lay in integrating various data sources, ensuring regulatory compliance, and transforming unstructured information into actionable insights.

Problem Statement

  • The client required a robust solution to integrate with its existing on-premise infrastructure while being FISMA and FedRAMP ready.
  • The solution had to handle diverse data sources, including PDFs, scanned images, CDs, USB drives, hard disks, SharePoint, and shared drives, audio files.
  • The goal is to provide predictive demographic analytics for the next several decades, leveraging this data over a 7-year pilot project.

Solution

GenAI Unicorn – The Product
GenAI Unicorn, our COTS (Commercial Off-The-Shelf) software with minimal customization, was proposed as the optimal solution. Designed to be FISMA and FedRAMP ready, it provided the client with a powerful platform to meet their objectives using the below platform capabilities:

  1. Data Integration Layer (ETL): Implemented a robust ETL layer to integrate data from sources like PDFs, scanned images, and SharePoint, etc.
  2. Automated Record Ingestion: Minimized manual effort by automating data input.
  3. Document Categorization: NLP-based classification (e.g., medical, legal, financial).
  4. OCR: Converted scanned images into searchable formats.
  5. Predictive Analytics: Provided demographic insights.
  6. Business Process Management (BPM): Automated workflows for accuracy and compliance.
  7. Security and Compliance: Met standards like Section 3101 of Title 44 U.S.C., FISMA, and FedRAMP.

Company

Federal Government

Industry

Government

Country

US

Key Drivers

Digitization, Workflow Automation, Document Management

Implementation: A Phased Approach

Phase 1: Data Integration Integrated diverse data sources (PDFs, scanned images, USB drives, SharePoint) into a centralized repository using the ETL layer. This phase ensured that the platform could process unstructured and semi-structured data seamlessly.

Phase 2: Data Transformation

  • Applied AI modules like OCR to convert data into readable formats.
  • Standardized metadata to enhance searchability and organization.

Phase 3: Categorization and Analysis

  • AI models classified documents into categories based on content analysis.
  • Automated workflows were created to process these documents for accuracy and compliance.

Phase 4(Ongoing): Digitization of Processes

  • Digitized user workflows to eliminate errors during data entry.
  • Enhanced the end-user experience with no-code features, enabling decentralized teams to collaborate effectively.

Business Impact

Metric

Year 1

Year 4

Annual Revenue

$10000

$ 2000000*

Data Source Integrated

2

25

Instance Deployed

1

4

Scale Ready Optimization

For 1 M Records

100M+ Records

Document Categorization Accuracy

Baseline

95%

Workflow Automation

0%

85% Digitized

*With per file pricing of upto US$0.15 and trillion of data records to be integrated, there is a significant upside over base implementation proposal of US$8mn.

Expansion to Additional Departments: Currently processing pilots in 4 departments, we aim to expand across all 50 divisions within the client. This is expected to generate annual revenues of around US$8mn by 2026 as per our fees quote proposal attached. During 2025, the expected revenues could reach around US$3-4mn.

Enhanced Predictive Capabilities: Future development includes AI modules for deep demographic forecasting and trend analysis.

Scalability: Scaling the system to manage over Multi Billion data endpoints.

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