AI Experience Audit
We test your chatbots, copilots, AI agents, and AI search the way real users use them. Then we tell you where they're failing.
Most AI experiences are deployed and then forgotten. The chatbot answers the easy questions and fails silently on the rest. The copilot suggests the wrong thing. The KB agent gives outdated answers. The AI search returns nothing useful. Each failure is invisible — until you measure it.
The AI Experience Audit tests every AI-powered touchpoint on your assets. Real conversations, real queries, real edge cases. We measure whether the AI understands the user, whether it resolves the request, whether it knows when to escalate to a human, whether it represents your brand consistently. We test the cases that go well, and the cases that go badly — because the badly cases are the ones costing you trust and conversions.
The output is a concrete improvement plan. Not a generic AI strategy document. Specific failures, specific fixes, prioritized by user impact.
What this audit covers
Chatbot intent coverage and accuracy
We test how often your chatbot actually understands what users mean. We map the most common queries, the variations users actually use, and where the bot misses or misinterprets.
Escalation logic and human handoff
When the AI doesn't know, what happens? We test escalation triggers, response times, context preservation when a human takes over. Most AI systems escalate too late, or lose the conversation context in the handoff.
Copilots and in-product AI assistants
For SaaS and digital products with AI features built into the experience. We test how the copilot helps users, whether it generates trust, whether users actually adopt it after the first try.
Knowledge-base agents and AI search
Help centers, documentation sites, and product search with AI. We test whether the agent finds the right answers, whether the sources are reliable, whether outdated content corrupts responses.
Response quality and brand tone
Even when the AI is correct, it can sound wrong. We test whether responses match your brand voice, whether they're clear, whether they avoid the generic "AI tone" that erodes credibility.
Hallucinations, edge cases, and risk
For LLM-based assistants. We test where the AI invents information, where it gives confidently wrong answers, where it could embarrass the brand or create legal exposure.
Proactive AI triggers and disruption
When the AI starts a conversation instead of waiting. We test trigger logic, timing, frequency — and whether proactive AI is helping users or annoying them into abandoning the page.
Common questions about this audit
Do you audit only chatbots, or also LLM-based assistants?
Both. Rule-based bots, LLM-based assistants, copilots, KB agents, AI search. The methodology adapts to the technology. For rule-based bots we look at decision trees and intent coverage. For LLM-based systems we look at response quality, hallucination risk, and tone. The core question is the same: does the AI actually help users, where does it fail, what should be fixed first.
We just deployed our AI. Should we audit it already?
Yes — sooner rather than later. Most AI failures happen at launch and never get caught because no one is measuring. An early audit catches the worst patterns before they erode trust at scale.
What if we use a third-party AI vendor?
That's actually a common case. Many companies deploy off-the-shelf AI tools and assume they work. The audit measures whether the vendor's solution actually fits your use case, your users, and your brand. The output gives you concrete evidence to take back to the vendor — or to switch.
Do you fix the AI for us?
No. We diagnose where it fails and recommend specific fixes. Your team, your AI vendor, or your development partner implements them. We don't write prompts, we don't tune models, we don't deploy code.
Do you test our analytics on AI interactions?
We verify that you're tracking AI interactions in a useful way. Most companies aren't — they have no idea how often their bot fails. If the tracking is missing, we flag it and recommend what to add.
How long does it take?
Two to four weeks, depending on how many AI surfaces we test and how much conversation history is available.
What you receive
- An AI performance map with intent coverage, accuracy scores, and failure patterns
- Conversation examples — what worked, what didn't, and why
- Specific rewrite suggestions for the most damaging failed interactions
- An improvement roadmap prioritized by user impact
- A 30-day follow-up session, included
Ready to scope this audit?
Request an auditReady to start with this audit?
The first call is free. Tell us what you suspect. We'll tell you if this is the right audit, or if a different one fits better.
Other audit areas
Conversion Journey Audit
We map every step from the first click to the final action. Then we tell you where users stop, and what it's costing you.
Digital Experience Audit
We test your site, app, or webapp the way your users actually use it. Then we tell you where the experience breaks down.
Lifecycle Signals Audit
We test every signal you send users outside the main product — emails, push, in-app messages, retargeting. Then we tell you if they're helping conversion or generating noise.