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AI engineering case study

HR & Finance Operations Copilot

Core stack

LangChain · Azure OpenAI · Databricks · ETL

Delivery approach

  1. Situation

    Employees repeated HR, finance, payroll, and policy questions across several support channels. Agents searched multiple systems and reconciled documents with different owners and effective dates. Because the sources mixed general policy with employee-specific information, a shared index without access controls could expose sensitive data or quote an obsolete policy.

  2. Task

    Create one internal assistant that identified the request domain, retrieved current authorized evidence, and escalated cases that required human judgment. The targets were 40% of routine query volume and more than 200 support hours saved each month. Policy ownership, employee-level permissions, and an audit trail had to remain intact.

  3. Action

    ETL pipelines load approved policies and operational references into Databricks with canonical IDs, owners, effective and review dates, domain tags, and access metadata. LangChain routes requests to HR, finance, payroll, or general policy retrieval. Azure OpenAI receives context only after authorization and source filtering. General questions use cited policy passages; employee-specific requests call narrow tools that return only permitted fields. The assistant does not infer payroll or employment outcomes from general documents. Exceptions, disputes, and low-confidence cases go to the correct support queue with the conversation context attached. Evaluation covers policy versions, ambiguous language, cross-domain questions, permission boundaries, missing data, and escalation. Monitoring tracks unanswered topics, stale sources, authorization denials, citations, latency, and handoff outcomes. Document owners receive reminders before review dates expire.

  4. Result

    The assistant covers 40% of routine query volume and saves more than 200 support hours per month. Employees use one entry point for common questions, while support teams receive fewer repeated contacts and escalations with the prior context attached. Effective dates, scoped tools, and citations protect policy and employee-specific answers.

Technology stack

  • LangChain
  • Azure OpenAI
  • Databricks
  • ETL