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Data Analytics & Insights

Measure and explain performance.
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Operational analytics and AI performance in one place

Track usage, quality, and business impact from ready-made dashboards with no setup required. Drill from macro trends into the exact interaction that explains them.

 

Customisable dashboards and KPIs

Build dashboards around conversation volume, response times, automation rates, CSAT, containment, and ROI starting from 50+ prebuilt indicators, then analyse trends, agent performance, and metric evolution across charts and data tables.

Real-time visibility and drill-down

Move from macro trends into workflow, user, or intent-level detail to monitor accuracy, missed utterances, fallbacks, and sentiment by intent, flow, channel, or region and click into the exact interaction trail.

AI-powered conversation analysis

Extract sentiment, clarity, friction points, and outcomes automatically across AI-to-human, human-to-human live chat, and contact center conversations with custom metrics adapted to your business.

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Use insight to improve the system, not just report on it

Analytics that is helping your teams tune the system, not just look backwards at what happened.

 

Friction detection and resolution

Identify where and why conversations break down by classifying friction intents, obstacle types, and the exact dialogue step causing difficulty so teams can fix the flow instead of the symptom.

Outcome tracking and enrichment

Define business outcomes per use case and measure them automatically, while the AI Analyzer proposes new outcomes and enriches the outcome dictionary when unmatched conversations appear.

Prompt engineering

Tune prompts, grounding, and retrieval strategies with visibility into latency and quality metrics, then tie automation to cost savings and revenue through ROI widgets and BI exports.

Objective agent performance evaluation

Score AI agents and human agents against the same metrics across every conversation, then route underperforming interactions for review, reassignment, or retraining.

Full conversation history

Replay interactions, inspect extracted metrics, and analyze improvement opportunities with full context preserved, including transcripts, step-by-step actions, and advanced audit trails.

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Frequently asked questions

Get answers to the most common questions about Druid's AI agents and the platform's analytics capabilities before your demo.

What is the analytics data pipeline architecture?

Every conversation, orchestration step, and system action generates structured telemetry that flows into the analytics engine in real time. Events are normalized, enriched with metadata (agent, channel, intent, outcome), and indexed for both aggregate dashboarding and per-interaction drill-down, with no separate setup required.

What KPIs are available out of the box?

50+ prebuilt KPIs including conversation volume, resolution rate, containment rate, CSAT, average handle time, automation rate, first-call resolution, escalation ratio, response latency, revenue attribution, and cost savings. All are filterable by agent, channel, flow, intent, time period, and geography.

How does friction detection identify root causes?

The system classifies friction intents, obstacle types (knowledge gap, flow error, integration failure, disambiguation failure), and the exact dialogue step causing difficulty. This lets teams fix the root cause directly in the flow, knowledge base, or integration rather than treating with manual workarounds.

What conversation replay and annotation capabilities exist?

Full interaction replays with preserved context: transcript, extracted entities, step-by-step Conductor actions, retrieved knowledge, confidence scores, and audit trail. Analysts can annotate responses, flag errors, and feed corrected utterances directly back into training sets for model retraining and flow improvement.

How does the ROI attribution engine quantify automation value?

The engine ties automation rates, containment metrics, and resolution times to configurable cost models like agent salary baselines, per-interaction costs, and revenue-per-conversion values. It calculates savings per agent, per flow, and per use case, with exportable reports for finance and executive review.

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