A RAG powered assistant cuts manual visa query effort by up to 40%
An AI assistant answers visa entitlement questions straight from official documents, with every answer citing its source, for a leading global immigration technology company.
The Opportunity
Users needed instant answers from complex visa entitlement documents, but resolving each question by hand was slow and needed expert involvement.
As the volume of questions grew, human only support became unsustainable.
The Transformation
Visa entitlement documents were parsed into semantic segments and embedded using Azure OpenAI, stored in a Pinecone vector database.
When a user asks a question, the system finds the segments that answer it, and Azure OpenAI synthesizes a response, with a citation back to the source document on every answer.
Documents Parsed Into Semantic Segments
Visa entitlement documents were broken into semantic segments so the system can retrieve the exact passage relevant to a question, instead of returning a whole document for a user to search through.
Retrieval Augmented Answers, Source Cited
A user's question becomes an embedding, matched against the segment library in Pinecone. Azure OpenAI then synthesizes an answer that cites the exact source passage it came from, on every response, not just some of them.
Built to Scale to the Full Document Library
The pilot proved the pattern on a working set of entitlement documents. The same pipeline was extended to the complete document library without rearchitecting the approach.
Less Manual Effort, More Trust
A meaningful drop in manual query resolution effort, and answers that cite their source give users a concrete reason to trust what the assistant tells them.
Reduction in manual query resolution effort, observed in the POC
Answers generated with a citation back to the source document, by design
Lower cost per contact once scaled to production
Increase in user satisfaction from faster, source cited answers