AI Customer Support Platform
A reference architecture for an AI support layer that resolves routine tickets and routes the rest with full context.
This is a solution blueprint authored by our engineering team. It describes how we would approach this problem — it is not a record of a delivered client project, and the expected benefits below are design goals rather than measured results.
Support teams absorb high volumes of repetitive questions — order status, password resets, billing queries — while genuinely complex issues wait in the same queue. Hiring scales cost linearly with volume, and knowledge lives in scattered documents agents cannot search quickly.
A retrieval-augmented support assistant sitting in front of the existing helpdesk. Company documentation, past resolved tickets, and product data are embedded into a vector store; the assistant answers from that corpus with citations, and hands off to a human with a full conversation summary when confidence is low or the intent is sensitive.
Ingestion pipeline chunks and embeds knowledge sources into a vector database, refreshed on change. A retrieval service assembles context per query, and an LLM gateway handles prompting, streaming, guardrails, and fallback between providers. A confidence threshold plus intent classification decides answer-vs-escalate. All conversations are logged for evaluation, and a review loop feeds corrected answers back into the corpus.
- Deflection of repetitive, well-documented queries
- Faster first response outside business hours
- Consistent answers grounded in approved documentation
- Human agents receive full context on escalation
- Support cost decoupled from ticket volume growth
Stateless retrieval and inference services scale horizontally behind a queue. The vector store is sharded by tenant or product line, and responses are cached for common questions. Provider fallback keeps the assistant available if a single LLM vendor degrades.
- PII redaction before any text reaches a model provider
- Tenant isolation at the vector-store and API layer
- Prompt-injection guardrails on retrieved content
- Full audit log of prompts, retrieved sources, and responses
- Configurable data-retention and right-to-erasure handling
Estimated 10–14 weeks for a production pilot on one support channel