HR & talent
TalentMind AI
Grounded AI assistance across an HR platform
An HR platform with automatic meeting summaries and a chat assistant whose answers are retrieved from company policy rather than recalled by the model.
This example comes from the technical delivery experience behind AussieSync. It does not necessarily represent a project contracted directly through AussieSync.
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01 Problem
HR teams lost hours summarising meetings by hand, and a general-purpose assistant could not be trusted to answer policy questions.
- Every interview and review had to be written up manually, days after the conversation.
- Policy answers lived across scattered documents nobody could search meaningfully.
- A plain LLM confidently invented policy that did not exist, unusable in an HR context.
- Anything touching employee records had to be explainable after the fact.
02 The solution
A retrieval-augmented design: documents are embedded and indexed, and every answer is grounded in what was actually retrieved.
- Meeting audio is transcribed and summarised into structured, reviewable notes.
- Company documents are chunked, embedded and indexed for semantic search.
- The assistant answers only from retrieved context, and cites the source it used.
- Low-confidence retrievals escalate to a human instead of producing a confident guess.
03 Technical approach
Every choice earns its place.
- LangChainRetrieval and prompt orchestration over the document index.
- Vector storeSemantic search that decides what the model is allowed to see.
- OpenAI APISummarisation and answer generation over retrieved context.
- n8nRoutes summaries and approvals into existing HR workflows.
04 Outcome
Hours of manual summarising eliminated, and instant grounded answers from company knowledge.
- Hours savedper week on manual meeting write-ups
- Groundedanswers cite company documents, not model recall
- Escalatedwhen uncertain, rather than guessed