The client’s process of extracting information from domain-specific documents and manually generating summaries for their end customers was time-consuming and inconsistent. The lack of an automated system often led to delays in report generation, increased error rates, and reduced the overall quality and timeliness of insights being shared with stakeholders.
They also faced roadblocks in generating actionable cybersecurity reports to support internal IT policy development. Risk and vulnerability assessments were fragmented, resulting in limited visibility into system weaknesses. Without a centralized reporting mechanism, policy creation remained reactive and lacked alignment with industry standards.
We sat down with the client’s team to understand their pain points. To address these challenges, our AI developers designed a comprehensive solution with the following key components:
Integrated a Large Language Model to automatically extract and summarize domain-specific information, reducing manual dependency while enhancing report consistency and accuracy.
Deployed an LLM-based solution to analyze cybersecurity data, highlight system risks, and recommend actionable next steps. The reports served as a foundation for defining IT policies aligned with organizational goals.
Built a structured IT policy framework informed by insights from cybersecurity reports. This framework ensured proactive risk management and compliance with industry best practices.
Redesigned the application interface to offer intuitive user flows, simplified navigation, and enhanced usability, especially for non-technical users handling the reports and data.
Enabled AI-based, real-time support within the platform using the LLM, allowing users to query and retrieve relevant data instantly, thereby improving customer satisfaction and reducing support load.
Data Collection & Structuring:
Used Python (pandas, NumPy) to clean and format raw input data to prepare it for LLM processing.
LLM Integration:
Integrated GPT-4 APIs to process and transform domain data into summarized insights and cybersecurity intelligence.
Database Schema Design & Storage:
Designed a scalable SQL schema for storing processed outputs. Used SQLAlchemy to manage data ingestion into the database layer.
Workflow Automation:
Automated the entire pipeline using Apache Airflow to ensure regular updates, real-time availability of processed insights, and minimal human intervention.
Reporting & Dashboards:
Developed data visualization dashboards using Power BI and Python-based plotting libraries to offer stakeholders a real-time view of key metrics and summaries.
30% Reduction in Preprocessing Errors:
Cleaning and formatting data with Python libraries improved compatibility and reduced the time spent on manual data preparation.
95% Accuracy in Summary Generation:
The LLM integration delivered near-human-level accuracy in summarizing domain content, replacing previously inconsistent manual summaries.
40% Faster Processing Efficiency:
Automated information flow from data collection to summarization led to a significant drop in processing time.
90% Reduction in Manual Effort:
Automated data ingestion and transformation using Python and SQLAlchemy minimized reliance on manual data handling.
25% Improvement in Query Response Time:
Optimized database design allowed the system to handle over 100,000+ records with improved speed and reliability.
35% Faster Report Generation:
Workflow automation and visualization tools like Power BI cut down report generation timelines, enabling quicker decision-making and faster IT policy implementation.
By integrating LLMs, Python, and a scalable database, the client replaced manual workflows with an automated AI pipeline, enabling faster summarization, real-time insights, and smarter decision-making. This shift reduced overhead and positioned them to deliver high-quality digital experiences.
Technology & Software
North America
End to End Project Lifecycle Management
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