We crawl every help source—FAQ pages, internal wikis, policy manuals, Jira resolutions, marketing emails—and slice them into small, citation-ready passages. Each chunk is tagged with metadata such as product, locale, effective date, and compliance owner. A single, cleansed corpus emerges.

We convert each passage into dense embeddings (OpenAI text-embedding-3-small or Sentence-BERT) and store them in Pinecone or Weaviate, enabling sub-100 ms similarity search. Hybrid keyword + vector recall ensures the system can surface a precise clause about “two-factor reset in France” or “chargeback window for debit cards.”

A guarded LLM sits behind a tight system prompt: “Answer only when a retrieved passage is cited; otherwise ask for clarification or escalate.” When a customer asks “Why was my card declined at the grocery?” the bot retrieves the correct clause, adds the real-time transaction code from the core banking API, and replies with an inline citation.

A widget drops into web and mobile apps; the same backend is exposed as a REST endpoint for WhatsApp, SMS, and IVR deflection (“Press 1 to get a chat link”). Regardless of channel, customers receive identical, policy-grounded answers.

For the top-five repetitive tasks PIN reset, card reissue, address change, KYC upload, balance query the LLM issues function-call payloads to backend APIs, completing the job without human touch. Edge-cases automatically generate structured Zendesk tickets that include a summarized conversation, sentiment score, and risk flags.