Voice Sales Assistant
QPOI built a voice-first AI assistant for a wholesale distribution software provider, letting field sales reps ask questions about their accounts and get spoken answers grounded in live sales data.
The challenge
The client's platform already computes account-level sales insights for wholesale distributors: which accounts are declining, where margin sits against peer benchmarks, what a customer has stopped buying. Getting value out of them meant reading dashboards, and the people who need them most are field reps who spend the day driving between customer visits.
They wanted reps to be able to just ask: a conversational assistant that answers questions about any account in a rep's patch, hands-free, from live data. And because the answers get repeated in front of customers, a wrong figure is worse than no figure. The assistant had to be built so it cannot invent a number.
- Architecture proposal and technical discovery
- Voice and chat interface end to end
- Grounded answer pipeline over live data
- Secure deployment to client infrastructure
Core features
Speech-to-speech conversation with barge-in: the rep can talk over an answer or tell it to stop, like a real conversation. A typed chat interface shares the same pipeline.
The model never writes SQL. It selects from a set of approved, parameterised query tools, and the server owns every query that touches the database.
Every figure, date and name in an answer must trace back to data that was actually served. A validation layer checks each generated answer, with a deterministic fallback so a correct answer goes out even when the phrasing fails the check.
Each rep sees only their own accounts. Scope is pinned server-side per session, and restricted fields are stripped before anything reaches the model or a cache.
Engineering fundamentals
Answer latency and database throughput measured against live data, with typical answers in under three seconds and headroom quantified before deployment.
An offline suite of golden cases runs with no API key, and a live evaluation replays the full approved question set against the real model to catch what fixtures cannot.
A hardened Docker image served over HTTPS, sign-in against the client's existing user store, and observability down to per-stage timings and full transcripts.
Outcome
Deployed to the client's infrastructure, answering from their live database
Every question in the client's signed-off scope answers against real data
Voice and text share one grounded pipeline, so a spoken figure is as trustworthy as a written one
Backed by over 700 automated tests plus a live-model evaluation suite
The pipeline stage by stage, the measurement methodology, and the faults the process caught before users could.