AI Chatbot Audit and Optimization
Long-term mentorship on AI chatbot design and development — consistent sessions, honest feedback, real projects.
Chatbots that launched 12 to 18 months ago often accumulate problems that were not visible at launch: intent misclassification rates above 18%, outdated knowledge base entries, broken API calls to services that have since changed their endpoints, and dialogue flows that no longer match current product offerings.
This audit service produces a clear picture of where the bot currently stands and what to fix first. It does not assume the bot needs to be rebuilt, though sometimes the audit reveals that it does.
Audit coverage
The technical review covers intent recognition accuracy across a random sample of 300 recent conversations, fallback trigger frequency, API failure logs, and response latency. The conversational review evaluates drop-off points, unresolved query patterns, and escalation rates to human agents.
Output is a 20 to 35 page written report with findings grouped by severity: critical, moderate, and low priority. Each finding includes a specific recommended action, estimated implementation effort in hours, and expected impact on resolution rate.
Optional add-on
Implementation support is available as a separate engagement. The audit team can stay on for 4 weeks to oversee execution of the top-priority recommendations.
What's covered
How the mentorship unfolds
Each stage builds on the previous one. There's no jumping ahead — the sequence exists for a reason.
Audit Methodology
- Conversation log analysis — sample of 300 recent sessions, intent accuracy measurement, drop-off mapping
- Technical infrastructure review — API health, response latency, error rate logging
- Knowledge base assessment — outdated content identification, coverage gap analysis
- Dialogue flow evaluation — dead end detection, escalation path audit, fallback logic review
- Benchmark report — current performance baseline across 7 defined metrics
- Prioritized recommendation report — 20 to 35 pages, severity-ranked, effort-estimated
The audit does not require access to model weights. Read access to conversation logs and API documentation is sufficient.
Still deciding?
Most people spend more time researching than it takes to finish the first module. Drop a question through the contact page and get a straight answer before committing.