Erg'OH — AI-Powered Ergonomics Knowledge Base
Business process automations using Retrieval-Augmented Generation to provide ergonomics and health suggestions. A proprietary AI-powered knowledge base interrogatable via API or conversational chat.
The Challenge
Erg’OH is an ergonomics consultancy whose expertise lives in a body of specialist knowledge that takes years to develop. The challenge was to make that expertise more scalable — enabling clients and partners to access accurate, context-aware ergonomics guidance without always requiring direct consultant time.
The solution needed to be trustworthy (ergonomics advice with safety implications must be accurate), accessible (API-callable by other systems as well as conversational), and genuinely useful as a standalone tool rather than a novelty.
The Approach
We designed and built a Retrieval-Augmented Generation (RAG) system around Erg’OH’s proprietary knowledge base. Key work included:
- Structuring and ingesting the specialist ergonomics knowledge corpus in a format optimised for retrieval
- Building the RAG pipeline combining embedding-based retrieval with generation via OpenAI and Gemini
- Developing the API layer for programmatic access by third-party systems
- Building the conversational chat interface for direct user interaction
- Deploying the full stack on Google Cloud Platform with appropriate scaling and access control
- Iterating on retrieval quality to ensure responses were accurate and appropriately caveated
The architecture was designed so the knowledge base can be updated by the client without requiring re-engineering of the system.
The Outcome
- Proprietary AI knowledge base live and accessible via API and conversational interface
- Ergonomics guidance available programmatically for integration into third-party workflows
- Knowledge base architecture supports ongoing updates without system changes
- Consultant time freed from routine information requests, focused on complex engagements
Technologies & Methods
Python, RAG architecture, vector embeddings, OpenAI API, Google Gemini, Google Cloud Platform (Cloud Run, Cloud Storage), REST API design, conversational UI.
About this engagement
This was a Build engagement: designing and building a production AI system around a specialist knowledge base, with both API and conversational interfaces. The focus throughout was on accuracy and practical utility rather than demo-ware.