Software and AI case study
How we tested messy multi-turn conversations before trusting the journey
Real customers correct themselves, change topics, omit device details and return later. We test those behaviours before an AI-assisted journey can influence a hand-off or suggested next step.
The risk we tested
A neat one-message demo can hide failure modes. A customer may start with a slow laptop, mention a suspicious pop-up, correct the model, then ask about data recovery. The system must preserve useful context without treating an early guess as fact.
- Corrections must replace an earlier assumption.
- Uncertain intent must trigger a clarifying question, not a diagnosis.
- Safety signals such as scams, smoke, swelling or data risk must take priority.
- QuoteMe receives only customer-confirmed context and never auto-submits a request.
How the test set works
We use scripted conversation families rather than one happy path: missing details, contradictory details, pronouns, spelling mistakes, multiple devices, topic switches, repeated questions and explicit “that is not what I meant” corrections. Each scenario has expected safety, routing and hand-off outcomes.
Automated checks catch regressions, while a human reviewer assesses whether the wording is understandable and whether the next action is genuinely useful. A pass means the bounded scenario behaved as expected; it is not a claim that every future conversation is solved automatically.
Release gate
We do not promote a journey from test to customer use only because a model response sounds fluent. The release gate checks structured output, allowed actions, privacy boundaries, fallback behaviour, cost limits and a safe human-help path. Failed or ambiguous cases stay reviewable and do not silently become business rules.