A Fortune-500 Listed and Leading Metallic Manufacturing Company Accelerates Operational Intelligence Through AI Automation

Location: North America

Industry: Global Manufacturing and Infrastructure

Key Highlights

  • Designed a custom feedback solution for agents with structured logging, resulting in improved user participation.(Refer accomplishment 1 in PPT)
  • Enabled marketing teams to access and discover relevant information quickly
  • Automated workflows for processing documents and identifying business leads
  • Automated extraction and tracking of project details from uploaded documents
  • Improved agent response accuracy with structured CSV integration
  • Developed scalable automation for detecting and logging data anomalies
  • Developed Agent for converting raw scripts to ALM format.(Refer Accomplishement 7)

Backstory

Known for its legacy over a century and marked among the Fortune-500, the company recycles, manufactures, and markets metal products and its products to the industries across the nation. It is also known for delivering exceptional value to its customers, suppliers, and investors alike across the globe.

As a global leader in manufacturing, its data has been fragmented across, and manual workflows and operations couldn’t meet today’s technological pace.

Challenge with Manual Operations

The organization had a tough nut to crack – critical business data was growing in volumes, and the information was trapped in unstructured formats, lacking the friction with evolving business intelligence:

  • Manual lead processing: Sales and marketing teams invested excessive time manually reviewing large volumes of data to identify potential business leaders, pertaining to the risk of delayed opportunities in the markets.
  • Fragmented feedback loops: The existing systems lacked feedback data as they were limited to a certain retention time. This scenario created chaos in enriching the user performance based on real-time production usage.
  • Unstructured technical data: Most of the engineering architectural blueprints and test scripts were often stored as images in the formats of HTML-heavy files. These files required manual data entry for analysis and reuse.
  • Data integrity issues: Inconsistent data patterns were observed in shared files that led to unreliability and inaccurate reporting.

Unified AI and Automation Ecosystem Led By Our Exceptional Engineers and Architects

To address these challenges, a comprehensive digital transformation strategy was implemented, focusing on three core pillars: Automated Intelligence, Structured Feedback, and Data Transformation.

1. Automated Lead Generation & Project Intelligence

Understanding the concerns, our engineers have developed automated workflows with an AI agent that analyzed the texts and thousands of documents to process the data.

  • Intelligent Extraction: The system extracts project names, locations (city/state), and product-specific keywords in minutes.
  • De-duplication & Logging: The solution compares new data with existing records, smartly updating the numbers for repeated projects and handling missing or partial details.
  • Real-time Alerts: As the high-relevance lead is detected, the agent creates a concise summary, and the original document are automatically sent to the managers for further actions.

2. Enhanced Agent Visibility & User Experience

The internal “Florek” AI agent was upgraded with a custom feedback and conversation logging architecture.

  • Structured Logging: Every interaction including the full user request and agent response is now captured in a persistent format. It has full visibility into production stage.
  • Contextual Feedback: A structured feedback mechanism enables users to provide intentional input that includes descriptions for inaccurate responses.
  • Expanded Knowledge: The agent capabilities are extended to include marketing-specific knowledge, allowing employees for rapid access to content discovery tools and references.

3. Streamlined Data Transformation Tools

Custom Copilot agents were built to handle specialized, time-consuming data conversion tasks:

  • OCR-Based Table Extraction: A new agent automates the upload of engineering tables, and it performs OCR-based extraction to generate structured data instantly.
  • Test Script Standardization: An automated agent extracts fields from raw, HTML-heavy test scripts and converts them into clean, structured plain text for ALM (Application Lifecycle Management).

Business Outcomes

The implementation of these AI-driven solutions delivered immediate and scalable impact:

  • Rapid Adoption: Within the first five days of production, the system captured over 100 interaction entries and more than 20 detailed feedback logs.
  • Operational Speed: Manual lead identification was replaced by real-time automated processing, ensuring faster responses without manual document review.
  • Improved Accuracy: Automated anomaly detection and primary key uniqueness checks (e.g., using order numbers) eliminated duplicate records and improved data reliability.
  • Scalability: The standardized logging and transformation patterns are now being reused across all new internal agents, reducing future development effort and risk.

Conclusion

By bridging the gap between raw data and actionable insights, the organization has moved from manual, error-prone processes to a streamlined, AI-enabled operation. This transformation not only empowers teams to focus on high-value tasks but also ensures leadership has a clear, data-driven view of production behaviour and market opportunities.

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