Case Study: Geospace Leverages Allganize Gen AI Platform to Preserve Institutional Knowledge and Drive Productivity

The Challenge: Leveraging Decades of Expertise while Preserving Data Sovereignty and Minimizing IP Risk

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Geospace Technologies, a global technology and instrumentation manufacturer specializing in advanced sensing, IOT and highly ruggedized products, faced a significant challenge common to many established organizations: the impending loss of invaluable institutional knowledge due to an aging workforce. Critical information, project histories, engineering insights, and data analyses were often siloed, residing in individual email accounts, presentations, and disparate file shares (including G Drive and on-premise file shares) – some belonging to employees who had already left the company.

This "tribal knowledge" was difficult to access, making it time-consuming for current employees, especially younger engineers, to find answers to internal questions regarding projects, past analyses, and product information. The lack of a formal, centralized knowledge base meant hours could be wasted searching for information that might exist somewhere within the company's vast data stores, estimated to be in the petabytes (with an initial focus on 30TB and a potential enterprise-wide scope of 500TB).

Key Pain Points:

• Operational Risk from Knowledge Attrition: Recent and impending retirements created a high-severity risk of losing non-recoverable engineering IP and project history, directly impacting future R&D cycles and compliance.
• Inefficient Information Retrieval: Current and new employees struggled to find answers to internal questions across the 500TB+ of data, spending excessive time searching through emails, old presentations, and scattered files.
• Lack of Centralized Knowledge Base: No system existed to consolidate and make accessible the company's 500TB+ of accumulated data and collective intelligence.
• Scalability Concerns: Existing methods of information sharing were not scalable to handle the sheer volume of data (tens of terabytes) and the needs of even the 100 initial users.
• Security and Access Control: The need to protect sensitive intellectual property (IP) and export-controlled data was paramount, necessitating full data sovereignty and an on-premise requirement.

Geospace was already exploring solutions, utilizing the ChatGPT Enterprise API, but hadn't yet trained it on their specific internal data. They recognized the need for a more tailored approach, ideally an on-premise Large Language Model (LLM) built upon their own product information and historical data.

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The Solution: Allganize's Enterprise AI for Comprehensive Knowledge Management

Geospace Technologies identified Allganize as a potential partner to address their knowledge management challenges. Allganize proposed leveraging its enterprise search capabilities, powered by an LLM, to ingest Geospace's vast historical data, including old emails, presentations, engineering drawings, and documents from various sources. The goal was to create a system where employees could ask natural language questions and receive accurate, context-aware answers based on the company's internal information.

David Witt, Senior Vice President Information Technology & CISO at Geospace shared: "We have a full AI roadmap that starts with connecting all of our data sources - structured and unstructured - and builds ROI-focused automation across engineering, manufacturing, sales, operations and finance. Allganize's platform is uniquely able to deliver on this vision without compromising our data and IP."

Allganize's Solutions & Approach:

1. Enterprise Search & LLM Implementation:

• Data Ingestion: The core of the solution involved ingesting Geospace's petabytes of data from on-premise file shares, G Drive, and email archives. This included information from past and present engineers.
• Knowledge Repository: Transform this ingested data into a searchable and queryable knowledge base.
• Question & Answering: Enable employees, particularly new and younger engineers, to ask questions in natural language and receive relevant answers, effectively creating a "Virtual Assistant" for internal knowledge. This would drastically reduce the time spent searching for information.
• Support for Structured and Unstructured Data Types: The system needed to handle diverse file types, including engineering drawings and other applications, as well as ERP data through MCP-based integrations.

2. Deployment Flexibility (SaaS POC to On-Premise):

• Initial SaaS Proof of Concept (POC): To facilitate a quick start and allow Geospace to experience the technology's benefits with a low barrier to entry, a SaaS POC was proposed. This allowed the Geospace team to navigate the learning curve before committing to a full on-premise deployment. The POC focused on a dataset of approximately 10GB.
• Production On-Premise Deployment: The ultimate deployment model for production was an on-premise LLM, not available in other solutions explored. This ensures data remains within Geospace's infra
• structure with full data sovereignty.

3. Addressing Specific Needs:

• Integration with Mission-Critical/Legacy Systems: Allganize’s ability to work with all parts of the enterprise stack - from legacy systems like Oracle / JD Edwards to network file storage - was critical in ensuring all data, structured and unstructured, is leveraged by the new AI platform.
• Access Control & Security: The platform allows for controlling user access and data access, including the ability to flag IP to limit control, addressing Geospace's security and export control concerns.
• Scalability: The proposed solution was designed to be scalable to handle Geospace's large data volumes and user base.

Expected Outcomes & Benefits for Geospace

By implementing Allganize's enterprise search solution, Geospace Technologies targeted several key benefits:

• Preservation of Institutional Knowledge: Capturing and making accessible the valuable expertise of retiring and past employees.
• Increased Employee Productivity: Enabling engineers and other staff to quickly find answers to internal questions, significantly reducing time spent on information searches.
• Improved Onboarding: Providing new hires with a powerful tool to rapidly learn about past projects, data, and internal processes.
• Faster, Data-backed Decision Making: Facilitating easier access to historical data and analyses to inform current projects and strategies.
• Secure and Controlled Access: Maintaining control over sensitive data through an on-premise deployment with robust access management features.
• Enterprise-wide AI Automation and Enablement: Improving productivity by speeding up processes and workflows across all functions - from R&D to Operations, to Sales and Finance - through a holistic AI platform that can grow with Geospace's data and user needs.

Conclusion:

Geospace Technologies' decision to partner with Allganize demonstrates a proactive approach to tackling the critical challenge of knowledge management in a complex, data-rich environment. By leveraging Allganize's advanced AI-powered enterprise search, Geospace is poised to transform its vast historical data from a dormant archive into a dynamic and accessible asset, empowering its workforce and safeguarding its invaluable intellectual capital for the future. The phased approach, starting with a SaaS POC and moving towards a full on-premise deployment, reflects a strategic and practical path to achieving their long-term knowledge management goals.

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