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Savelind
Savelind
Analytics & Monitoring
Chatbot Analytics

Measurable performance, not just conversations

Chatbot metrics often sit in dashboards nobody opens. Savelind works directly with you to identify which numbers actually reflect user intent — and what to do when they drift.

Request a session About Savelind
4.7 Avg. rating
119 Clients served
9yrs In operation
Savelind analyst reviewing chatbot performance data on a monitor

One analyst. One dedicated client. Every metric explained in plain language.

What changes when you actually measure

From raw data to decisions

Containment rate

38% 67%

Conversations resolved without escalation to a human agent — the first metric worth watching in any deployment.

Avg. turns to resolution

11 5

Fewer conversational turns for the same outcome — a reliable sign that intent recognition is calibrated correctly.

Fallback trigger rate

29% 7%

Every fallback is a signal. Tracking frequency and clustering by topic reveals where the training data has gaps.

Performance category distribution — typical client profile after 8 weeks

Intents matched
90%
Flow completed
83%
User satisfied
81%
Zero fallback
77%
Strong performance
Needs tuning
Requires retraining

Direct work, not ticket queues

"your data, explained by a person"

When a chatbot's containment rate drops without warning, the usual response is opening a support ticket and waiting. At Savelind, you speak directly with the analyst who reviewed your logs. No intermediary. No templated recommendations that miss your specific context.

Every client gets a structured monitoring plan based on their chatbot's actual conversation volume and domain. Retail customer service bots, internal HR assistants, and lead qualification flows each need different thresholds and different response playbooks.

  • 01

    Baseline audit of existing logs

    We start with what you already have — conversation exports, fallback logs, and any CSAT signals — to establish a realistic baseline before suggesting any changes.

  • 02

    Metric selection for your context

    Not every chatbot needs the same KPIs. A sales qualification bot is measured differently from a support deflection bot. We identify the three or four numbers that actually predict success for your use case.

  • 03

    Ongoing monitoring with direct feedback

    Weekly or bi-weekly review sessions — your analyst walks through anomalies, explains what caused them, and proposes specific changes. No automated reports that sit unread.

  • 04

    Iteration and retesting

    After implementing training data adjustments or flow changes, we track whether the target metric moved. If it did not, we revisit the hypothesis rather than waiting for the next reporting period.

Common questions about chatbot monitoring

These are the questions clients typically ask before starting. Hover each item to read the full answer.

Savelind works with log exports and conversation data regardless of the underlying platform — Dialogflow, Amazon Lex, Rasa, Microsoft Bot Framework, and custom-built systems. If you can export conversation transcripts and intent classification data, we can work with it. Platform-specific integrations for real-time monitoring are discussed during the initial audit call.

It depends on conversation volume. Bots handling fewer than 200 conversations per week need at least three weeks of clean data before patterns become statistically meaningful. High-volume bots — over 2,000 weekly conversations — can produce reliable initial findings within five to seven days of starting the baseline audit.

Analysis and recommendations are the core service. Some clients choose to implement changes themselves and bring results back for review. Others prefer that Savelind's analyst drafts the specific training data additions or dialog flow adjustments — this is available as an extended engagement and scoped separately based on the size of the needed changes.

At minimum: conversation transcripts with intent labels and confidence scores, fallback event logs, and any satisfaction ratings collected. Personally identifiable information is not required and should be anonymised before sharing. Full technical specifications for data handover are provided after the initial scoping call. We do not require direct system access to begin analysis.

No fixed minimum. Some clients start with a single-session audit to evaluate a specific problem. Ongoing monitoring arrangements typically run month-to-month and are structured around your chatbot's release and review cadence. Engagements are adjusted as your needs change — no bundled packages that lock you into services you do not use.

Savelind has been working with conversational AI teams since 2016. An initial audit call is a straightforward way to assess whether the service fits your situation.

Book an audit call