With customers. With regulators. In production. And most teams don't see it until it's too late.
A user describes their situation in message one. By message four, the assistant responds as if the conversation never happened. Context decay is invisible in isolation — it only surfaces across full dialogues.
The assistant doesn't hedge or flag uncertainty. It states fabricated facts with the same fluency as real ones. Confident wrongness is harder to catch than obvious confusion — and far more damaging to trust.
They use slang. They change topic mid-conversation. They phrase things your test cases never anticipated. An assistant that passes structured tests can still fail catastrophically when a real customer arrives.
Traditional QA — manual or automated — assumes deterministic behaviour. The same input produces the same output. You write a test, it passes, you ship.
LLM-based assistants break that assumption entirely. The same question generates different answers. Conversations span multiple turns, channels, and agent workflows. And a single bad response can undo months of customer trust.
Most teams respond by testing what they can, documenting the gaps, and hoping for the best. That's not quality assurance. That's unmanaged risk.
Modern AI assistants don’t fail because they misunderstood an intent. They fail inside conversations.
They lose context across turns. They hallucinate facts with confidence. They sound correct, until they aren’t. And when they fail, it’s not in a test environment. It’s with customers, regulators, or the public.
That approach worked when software behaved predictably. It doesn’t work for LLM-based chatbots, voicebots, or AI agents.
Why? Because real users don’t follow scripts. Conversations don’t stay on a single path. And a single bad response can undo months of trust.
Hangar 5 exists to close that gap.
We believe quality assurance for GenAI must reflect how these systems are actually used - through full, end-to-end conversations, at real-world scale.
That’s why Hangar 5 simulates realistic interactions automatically.
Why we record every dialogue, not just scores.
Why we show failures exactly as customers experience them.
And why we focus on relevance, grounding, and user experience across entire conversations - not isolated turns.
We don’t help teams feel confident.
We help them prove it.
Because when AI goes live, hope is not a strategy. And “we tested what we could” is not an assurance model.
Hangar 5 exists so teams can ship conversational AI with evidence - not assumptions.
Book a 30-minute demo. We’ll run a live test on your chatbot, voicebot, or agent and you’ll have recorded dialogues, scores, and video replay before the call ends.