Edge Computing and Local LLMs: Redefining Database Queries and Field Operations
Data security and operational latency are two of the most significant challenges facing enterprise technology today. This fortnight, High Digital shares our latest architectural solutions for secure data querying and automated field reporting.
1. Querying Trillions of Records via Local LLMs
When dealing with large amounts of trade data, or customer data, routing queries through public APIs (like OpenAI) introduces unacceptable latency and severe data privacy risks. To solve this, Hanse utilises a local deployment of Meta Llama. We trained this model specifically on our proprietary data schemas and trade data use cases. This architecture ensures the AI behaves strictly according to predefined constraints, maintains zero-trust data security, and delivers near-instantaneous response times by eliminating external network calls.
2. Automating Field Operations with Computer Vision
Manual data entry in the field is a massive operational bottleneck. We engineered Fore-Site, a Progressive Web Application (PWA), to shift the reporting burden from manual text entry to visual AI processing. Fore-Site captures high-resolution visual data at the edge, utilising computer vision models to classify hazards and extract structured metadata. The context-aware AI then maps this visual data directly into JSON form fields via natural language generation, completing multi-minute compliance tasks in seconds.
Discover the engineering behind these applications on our technical blog.
https://www.highdigital.co.uk/blog/
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