Analytics Pipelines

This document covers the pipeline framework in Analytics and Insights: the built-in pipelines that power Agent Performance, Quality Monitor, and Customer Insights, and the visual node-based editor for building custom evaluation logic. Navigation: Project → Insights → Pipelines Each pipeline card displays its name, description, enabled/disabled status (green “Enabled” badge or gray “Disabled” badge), trigger count, and last processed timestamp (or “Never processed” if the pipeline hasn’t run yet). Use the search bar at the top to find pipelines by name.

Sections

Section	Purpose
Built-in	Pre-configured pipelines that ship with the platform, ready to enable with a single toggle.
Custom	User-defined processing workflows created using the visual pipeline editor.
Recent Runs	Pipeline execution history with timestamps, durations, status (success/failure), and links to output data.
Data	Pipeline output data available for dashboard integration, export, and downstream consumption.

Built-in Pipelines

The platform ships with eleven pre-built pipelines covering evaluation, classification, detection, and monitoring needs. Enable each pipeline with a single toggle — no configuration required for the defaults.

Pipeline	Description
Sentiment Analysis	Per-message sentiment scoring with conversation-level trajectory analysis. Feeds the Sentiment Trajectory chart in Customer Insights.
Intent Classification	Classifies conversation intent using LLM analysis with customer-defined taxonomy or auto-discovery. Feeds the Intent Distribution chart in Customer Insights.
Quality Evaluation	LLM-as-judge quality evaluation with configurable rubric dimensions. Feeds the Quality Score in Agent Performance and Quality Monitor.
Hallucination Detection	Detects unsupported claims, self-contradictions, and factual accuracy issues in agent responses.
Knowledge Gap Analysis	Identifies gaps in knowledge base coverage by analyzing retrieval precision and uncovered topics.
Guardrail Analysis	Evaluates guardrail effectiveness — detects false positives, false negatives, and bypass attempts.
Context Preservation	Evaluates whether agents preserve relevant user and workflow context through the conversation.
Friction Detection	Detects user frustration signals — rephrased questions, message escalation, caps, and exclamation patterns.
Anomaly Detection	Monitors analytics metrics for statistical anomalies using z-score and SPC (Statistical Process Control) charts.
Drift Detection	Monitors analytics metrics for gradual performance drift by comparing baseline and current windows.
Evaluation Run	Executes persona × scenario × evaluator matrix evaluation with bias mitigation and trajectory scoring.

Custom Pipelines

Custom pipelines let you define your own analytics logic using a visual, drag-and-drop node-based editor. Use custom pipelines to build organization-specific evaluation criteria that go beyond the built-in set — for example, regulatory compliance checks, brand voice adherence, or domain-specific accuracy scoring.

How the Editor Works

The editor presents a visual canvas where you construct a pipeline as a directed graph of connected nodes. A Node Palette on the left side provides draggable node types organized into categories:

Category	Node types
Data	Read Conversation, Read Message Window, Aggregate, Database Query, Filter, Transform Data, Inspect Output
Logic	Sub-Pipeline (reuse an existing pipeline as a step), Wait for Event (pause execution until a named event fires)

The core pattern: define a trigger, connect one or more processing nodes, then wire the output to a storage or metrics node.

Pipeline Structure

Component	Description
Trigger	Defines when the pipeline runs. Trigger types include Kafka events, scheduled intervals, in-platform events (for example, conversation completed), or filtered subsets of sessions.
Processing nodes	The evaluation and transformation logic applied to each triggered item. Nodes can read conversation data, query databases, aggregate metrics, filter records, transform data shapes, or call LLMs for evaluation.
Metrics output	The results the pipeline produces. Metrics take the form of named, typed values (counts, percentages, scores) that appear in the Data section and that you can wire into dashboards.

Pipeline Lifecycle

Pipelines follow a Draft → Validate → Test → Activate lifecycle. The editor toolbar shows the current state (Draft badge, “Validation passed” indicator) and provides buttons for Test, Validate, Save, and Activate. You can iterate on draft pipelines without disrupting live ones, save drafts as you go, validate to catch configuration errors before deployment, and run test executions to verify output before activating.

Sub-Pipelines

You can reference one pipeline as a step inside another, enabling reuse and composability. For example, a “Compliance Evaluation” pipeline could call both the built-in “Guardrail Analysis” and a custom “Regulatory Wording Check” as sub-steps.

Attaching to Dashboards

Once a custom pipeline produces data, its metrics appear alongside built-in metrics in Agent Performance, Quality Monitor, and Customer Insights, giving teams a single pane of glass across both standard and organization-specific evaluation.

Start with the built-in pipelines to establish baselines, then create custom pipelines for organization-specific quality dimensions. The validate-before-activate workflow catches configuration errors before pipelines go live.

Get Started

Build

Test and Evaluate

Deploy

Analytics and Insights

Administration

References

Sections

Built-in Pipelines

Custom Pipelines

How the Editor Works

Pipeline Structure

Pipeline Lifecycle

Sub-Pipelines

Attaching to Dashboards

​Sections

​Built-in Pipelines

​Custom Pipelines

​How the Editor Works

​Pipeline Structure

​Pipeline Lifecycle

​Sub-Pipelines

​Attaching to Dashboards

Sections

Built-in Pipelines

Custom Pipelines

How the Editor Works

Pipeline Structure

Pipeline Lifecycle

Sub-Pipelines

Attaching to Dashboards