LLM Customer-Support Automation
Support automation that answers from a company's own knowledge, in production.
- Role
- AI / backend engineer
- Year
- 2024
- Timeline
- ~4–6 weeks
A live product was drowning in repetitive support questions that all had answers somewhere (in docs, past tickets and product data), but nobody could find them fast enough, so the same questions kept reaching a human.
Answers had to be grounded in the company's own content rather than hallucinated, fast enough for a chat experience, and safe to put in front of real paying customers.
Grounded every answer with retrieval-augmented generation over the company's corpus.
Instead of Prompting a base model with no grounding.
Why: RAG kept answers tied to real, current content, the difference between a helpful assistant and a confident liar.
Used hybrid semantic + keyword retrieval, with guardrails.
Instead of Pure vector search.
Why: Hybrid retrieval caught both fuzzy questions and exact product terms, and guardrails stopped it answering outside what it actually knew.
Streamed responses inside the existing product.
Instead of A bolt-on chatbot widget in the corner.
Why: Streaming natively felt fast and trustworthy, and let the system hand off cleanly to a human whenever confidence dropped.
- Ingestion + embedding pipeline with real-time indexing
- Hybrid semantic + keyword retrieval
- Contextual chat with memory & guardrails
- Streaming responses in the product UI
- Human-in-the-loop escalation on low confidence
Hemant Manglani
Ahmedabad, India
Looking for someone who ships? Let’s talk.
I’m actively looking for a backend or AI engineering role. If your team needs an engineer who can own systems end to end, I’d love to hear about it.
Actively looking · Ahmedabad · on-site, hybrid or remote