An internal chatbot trained on company documents (handbooks, policies) to provide employees with instant answers to common questions, reducing the burden on HR and IT departments.
HR and IT teams answer the same questions again and again: how much leave do I have, how do I claim expenses, what is the parental leave policy, how do I reset my password. The answers usually exist in a handbook, intranet or shared drive, but staff cannot find them or do not trust that the document is current, so they message HR instead and interrupt more valuable work.
An HR chatbot built on retrieval-augmented generation answers these questions inside Slack or Microsoft Teams by searching your approved policies and quoting the relevant section. Staff get an answer immediately with a link to the source, and HR only handles questions that genuinely need a person. The chatbot is only as good as the documents behind it, so this project is often a useful prompt to tidy up policies too.
HR and IT gather current handbooks, policies, procedures and FAQs, remove outdated versions and assign an owner to each document.
Documents are split into sections and stored in a vector database such as Pinecone, with metadata for policy name, version and applicable locations or employee groups.
When an employee asks a question in Slack or Teams, the system retrieves the most relevant sections and an LLM writes a short answer citing them.
Questions about personal matters, grievances, health or anything the documents do not cover are redirected to the right HR contact.
HR reviews unanswered and low-rated questions weekly, updates documents, and the index refreshes automatically when policies change.
An employee asks the company's Slack bot, 'What is our PTO policy?' The bot, using a RAG system, pulls the relevant section from the employee handbook and provides a direct, accurate answer.
RAG, Vector Database (Pinecone), LLM (GPT), Slack/Teams Integration
Leave, pay and entitlement answers must match the Fair Work Act 2009, the National Employment Standards and your modern award or enterprise agreement, so the bot should quote your policy and link to the Fair Work Ombudsman rather than interpreting entitlements itself. Questions about bullying, harassment or psychosocial safety relate to work health and safety duties and should go to a person promptly. Staff questions may reveal personal or health information, so limit logging, restrict who can read chat history and follow the Privacy Act 1988 where it applies. Choose an Australian hosting region if your data policies require it.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Intranet search returns a list of documents; the chatbot reads the relevant sections and gives a direct answer in plain language, with the source attached. It understands questions phrased casually, such as asking whether you can take leave during probation, and is available where staff already work, in Slack or Teams.
Only if it is connected to your HRIS or payroll system with proper authentication, so each employee sees only their own data. Many teams start with policy questions only, which is simpler and lower risk, and add personal data lookups later once security and access controls are tested.
The technical build is fairly quick, but gathering and cleaning policy documents, setting access rules, testing answers against real employee questions and getting HR and legal sign-off take time. A pilot with one team helps identify gaps in documents before the bot is launched to everyone.
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