A secure, custom AI Agent trained exclusively on a company's in-house materials (training documents, frameworks, SOPs) to act as a 24/7 expert assistant for a highly specialized field like Critical Risk Management. Features role-based access for internal team vs. external client use.
Specialist consultancies in fields like critical risk management sell expertise that lives in long frameworks, control libraries, training packs and procedures. Clients and junior staff constantly need quick answers from that material, such as which critical controls apply to working at heights, but the answer is buried in a document they cannot find or do not have time to read. Senior consultants spend their days fielding the same questions by email, and clients sometimes fall back on generic internet advice that does not match the framework they paid for.
An AI knowledge base for risk management puts the firm's own material behind a conversational assistant. Instead of drawing on general internet knowledge, it retrieves the relevant passages from approved documents and answers from those, citing where each answer came from. Role-based access means internal staff can see the full library while each client sees only the material licensed to them. The consultants remain the authority; the assistant simply makes their written guidance available at any hour and in the words the question was asked, and it says so when the documents do not cover a topic.
Approved frameworks, SOPs, control libraries and training documents are collected, cleaned and tagged by topic, version and audience, such as internal only or client licensed.
Documents are split into passages and converted into embeddings stored in a vector database such as Pinecone or ChromaDB, keeping the audience tags with each passage.
When a user asks a question, retrieval-augmented generation finds the most relevant passages that user is permitted to see and passes only those to the LLM.
The LLM answers using the retrieved passages, cites the source document and section, and states plainly when the library does not contain an answer.
Questions, answers and user ratings are logged so consultants can spot gaps, correct weak answers and update the source documents rather than patching prompts.
A consulting firm provides its clients with access to an AI assistant. A client's safety manager can ask, 'What are the top 3 controls for working at heights?' and the AI will provide a precise answer based directly on the firm's proprietary frameworks, ensuring expert advice is always available.
OpenAI GPT models, RAG, Vector Databases (Pinecone, ChromaDB), Web Interface (Streamlit, React), Role-Based Access Control
Safety guidance sits alongside legal duties under the model Work Health and Safety laws adopted in most states and territories, so the assistant should present answers as guidance from your framework, not as a substitute for a site risk assessment or competent person. Your frameworks are copyright material, and the system should prevent clients from exporting the full library. Choose hosting and vector storage in an Australian cloud region if clients expect data to stay onshore, and confirm your LLM provider does not use submitted prompts to train its models. Questions may reveal incident details, so treat logs as confidential and cover them in your privacy policy.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Grounding answers in retrieved passages greatly reduces this, and the assistant can be instructed to decline when nothing relevant is found. It is not infallible, so citations are shown with every answer, letting the user check the source. Regular review of logged answers by consultants catches errors early.
Not if access control is designed properly. Each passage carries tags for its audience and client, and retrieval filters on those tags before anything reaches the model. Permissions should be tested with dummy client accounts before launch and rechecked whenever new documents or clients are added to the system.
The size and messiness of the document library, the number of distinct access roles, the choice of hosting and the level of interface customisation drive cost. Ongoing costs come from LLM usage, vector database hosting and the time consultants spend keeping documents current and reviewing answers.
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