Vantage — Support AI Assistant
RAG-powered assistant resolving 41% of support tickets automatically, integrated with their existing helpdesk.
- Timeline
- 4 months, live in production
- Team
- 2 AI/backend · 1 frontend · 1 support lead (client)
- Volume
- ~2,850 tickets per quarter
- Integration
- Existing helpdesk, no migration
Vantage's support team was not short of tooling. They were short of hours. The same questions arrived every day, and every one of them was answered by a person from scratch.
- Sector
- Logistics
- Region
- Europe
- Engagement
- AI assistant + helpdesk integration
A quarter's worth of tickets — around 2,850 — came in through email and chat, and roughly 38% of them were billing questions with a knowable answer. Invoice queries, proration, why a surcharge appeared. Answering them consumed the team's capacity, which meant genuinely complex logistics problems sat in the queue behind them.
The support lead had a firm and correct objection to AI in support: a confidently wrong answer about someone's invoice is worse than a slow correct one. Any assistant had to be able to say it did not know, and a human had to be able to see exactly what it based an answer on.
There was also a hard constraint on scope. They had years of history and workflow in their existing helpdesk and were not migrating off it. Whatever we built had to work inside it.
How the work was actually sequenced
Retrieval over their own documentation, not a general model
Answers are grounded in Vantage's help centre, billing policies and resolved-ticket history, chunked and embedded so the assistant retrieves the relevant passages before it writes anything. Every reply carries the sources it used, which is what made the support lead willing to turn it on.
An explicit 'I don't know' path
When retrieval confidence is low, or a question touches anything the assistant is not allowed to answer — refunds, account closures, disputes — it does not improvise. It hands off to a human with the conversation and the retrieved context attached. Escalation is a designed feature, not a failure state.
Inside the existing helpdesk, not beside it
The assistant works as an agent in their current tool. Drafts appear for agent review; only categories that passed a review period auto-send. No data migration, no second inbox, no retraining the team on new software.
Shadow mode before it ever spoke to a customer
For the first six weeks it answered every ticket silently and the support lead graded the drafts. That produced a concrete list of what it was reliably good at, which became the auto-resolve allowlist. Automation was earned category by category rather than switched on and hoped for.
Their AI assistant now handles 40% of our support volume. The RAG setup is genuinely accurate, and they were transparent about trade-offs the whole way.
AI
- LLM APIs
- Retrieval-augmented generation
- Embeddings
- Evaluation harness
Backend
- Python
- FastAPI
- PostgreSQL
- pgvector
- Celery
Integration
- Helpdesk API
- Webhooks
- Audit logging
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