Chatbot Conversation Analytics and Audit Logs
A chatbot you cannot review is a chatbot you cannot improve or defend. Logging is not a reporting extra bolted on at the end; it is what lets you find the wrong answers, and what you produce when someone asks what the bot told a customer.
Discuss your projectWhat you can review
Every conversation, in full
Searchable by date, customer, topic, or outcome. Including the ones that went badly, unedited — those are the valuable ones.
What the bot retrieved
For each answer, which passages it drew on and from which document. When an answer is wrong this tells you whether retrieval failed or your content is wrong, and those need different fixes.
Questions it could not answer
The most directly useful report. A recurring question with no match in your content is a documentation gap, and closing it improves the bot and your website at once.
Escalations and why
Which rule fired, how often, and what happened next — the evidence for tuning thresholds rather than guessing at them.
Where conversations stop
Abandonment mid-conversation, with the last exchange visible. Usually points at a specific answer that is sending people away.
Volume and cost
Conversations, messages, model usage and spend, by period. Dull, and the thing finance asks for first.
Compliance
Under GDPR a conversation containing personal data is processing like any other. That means a stated lawful basis, a retention period that is actually enforced rather than aspirational, and the ability to find and export or delete one person's conversations when they exercise their rights. We implement deletion as deletion, including from the search index and any derived logs — a record that survives in a secondary store is a breach of the same request.
If you operate in healthcare in the United States and a conversation could contain protected health information, the architecture changes rather than the configuration. That means a Business Associate Agreement with every processor in the path, including the model provider; encryption in transit and at rest; access limited and logged per user; and no PHI reaching a service not covered by a BAA. Some model providers offer this and some do not, which constrains the design. We will tell you plainly whether what you are asking for can be done compliantly before you commission it.
For financial services, the recurring requirement is an unalterable record of what was said, retained for a defined statutory period, with the ability to produce every conversation involving a given customer. We implement the log as append-only for that reason: corrections are added as new entries rather than edits, so the history cannot be quietly rewritten.
We are engineers rather than your compliance function. What we provide is a design that meets the requirement you give us, documented clearly enough that your own counsel or auditor can assess it. Tell us the framework at scoping, because retrofitting a logging design to satisfy an audit is far more expensive than building to it.
Turning logs into improvement
The reason to have this is not the dashboard. It is a monthly cycle that makes the bot measurably better.
- 01
Read the failures first
Unanswered questions and abandoned conversations, grouped by theme. Twenty minutes of this is worth more than any summary metric.
- 02
Decide which gaps are content
Most are. The answer does not exist in your documentation, or exists in a form retrieval cannot find. Both are fixed by writing, not by changing the bot.
- 03
Correct what was wrong
Where the bot answered from an out-of-date page, the fix is the page. Where it answered from the right page and still got it wrong, the fix is in chunking or retrieval, and we handle that.
- 04
Retune escalation
Triggers firing far too often, or not often enough, adjusted against the log rather than against impressions.
- 05
Re-measure
The same reports the following month. The number worth watching is the proportion of conversations resolved without escalation and without abandonment — not conversation volume, which mostly measures your marketing.
Common questions
Related in AI Chatbot Development
Tell us what you need
Describe the problem and we will come back with scope, timeline, and a rate. No obligation to proceed.
Get in touch