Analytics and Dashboards

Workspace-level analytics for call quality, pipeline health, data quality, and entities - plus built-in and embeddable dashboards and the Insights Agent.

Beyond the per-call analysis described in Call Intelligence, the platform provides workspace-level analytics covering call quality, data quality, pipeline health, and entity resolution. These views give operations teams visibility into how data flows through the system, where quality is trending, and where attention is needed - through built-in dashboards, embeddable components, and a conversational Insights Agent.

All analytics support date range filtering, time bucketing (hourly, daily, weekly), and optional service-level filtering. Results power the Developer Console dashboards and are available to any user with read access to the workspace.

Call Quality Trends

Analytics aggregate call intelligence data across all completed calls in a workspace.

View	What It Shows
Call quality	Quality score trends (avg, p50, p95), distribution by tier, escalation rate, call volume
Emotion trends	Dominant emotion distribution across calls, valence/arousal trends over time, per-emotion frequency
Safety trends	Escalation frequency over time, risk level distribution, safety rule match counts
Latency	p50/p95/p99 latency by component (engine response, audio time-to-first-byte, navigation, render)
Tool performance	Per-tool success/failure rates, failure trends, invocation counts and average duration
Operator performance	Escalation rate trends, quality comparison (escalated vs non-escalated calls), operator connect time

Advanced analytics support percentile breakdowns (p50/p95/p99) for duration and quality scores, time series trends with p95 latency, and breakdowns by service and call direction (inbound/outbound).

Period-over-period comparison lets you pick any two date ranges and see absolute and percentage change for each KPI. When you update an agent's context graph, change a prompt, or modify an escalation rule, you can compare the week before and after to see exactly what changed. This is the simplest way to answer "did that change help?" with data instead of intuition.

This is the feedback loop that drives continuous improvement. You make a change to the agent configuration. You wait for enough calls to accumulate. You compare the quality distribution before and after. The data tells you whether the change helped, hurt, or had no measurable effect. Without this, configuration changes are guesswork.

Call Detail and Historical Access

Call detail is available for the full retention period of your workspace. When a call is no longer held in the real-time voice layer, the platform reconstructs its detail from durable storage - combining persisted call state with post-call intelligence records. This means dashboards, analytics queries, and API consumers can access call metadata, quality scores, conversation summaries, and escalation data long after the call completes, without gaps caused by real-time layer turnover.

Call Statistics

For voice deployments, call analytics track volume, duration, and daily patterns. These metrics help operations teams identify capacity trends (peak calling hours, seasonal volume changes) and spot anomalies (sudden drops in call volume that might indicate a routing issue).

• Call volume - Total calls over configurable windows (30-90 days), broken down by service and direction (inbound/outbound)
• Duration distribution - How long calls last, useful for identifying calls that are too short (abandoned) or too long (stuck in loops)
• Daily breakdown - Per-day call counts for trend analysis
• Service breakdown - Volume by agent service, so you can compare usage across scheduling, care coordination, and other workflows

Surface Analytics

Surfaces are agent-generated data collection forms delivered to patients via email or web. Four analytics views provide closed-loop intelligence on how surfaces perform, enabling agents and gap scanners to optimize surface design based on actual outcomes:

Metric	What It Shows
Completion rates	Overall completion rate, trend over time, and breakdown by source (mid-call agent, gap scanner, manual). Identifies whether surfaces are actually being filled out.
Channel effectiveness	Per-channel (SMS, email, WhatsApp, web, voice) completion rate and average time-to-complete. Shows which delivery method works best for your patient population.
Field abandonment	Which specific fields cause patients to stop filling out a surface. Drop-off rate and save rate per field, so you can identify confusing or unnecessary fields.
Per-entity history	Surface history for a specific patient: completion stats, preferred channel, and recent surfaces. Useful for choosing the right channel and avoiding over-solicitation.

All surface analytics support date range filtering (default 30 days, max 90). The field abandonment data is particularly actionable - if 40% of patients drop off at a specific question, that question is either confusing, unnecessary, or too sensitive for the delivery channel. Removing or rewording it directly improves completion rates.

Event Distribution

Event analytics show how data enters the system and what kinds of data are flowing. This is useful for two things: verifying that integrations are working (confirming that a connector is producing expected event volumes) and understanding the data mix in the workspace.

Two views are available:

• By type - Event counts per entity type (patient, appointment, practitioner, insurance, medication, etc.). Shows what the system knows about and highlights gaps - if you expect appointment data but see zero appointment events, something is misconfigured.
• By source - Event counts per data source (EHR sync, voice extraction, manual entry, surface submission, etc.). Shows where data is coming from and how the mix changes over time. A healthy workspace typically has authoritative EHR data as the largest source, with voice-extracted and surface-submitted data filling gaps.

These distributions are useful during initial integration setup (to verify connectors are producing data) and ongoing operations (to spot when a source goes silent).

Data Quality Dashboard

The data quality dashboard tracks confidence distribution across all events in the workspace. Every piece of data that enters the world model carries a confidence score, and the dashboard shows how that distribution looks across your entire dataset:

Bucket	Confidence Range	What It Means
Rejected	0.0	Events that failed review or were explicitly contradicted
Raw	0.1-0.3	Unverified data from agent inference or initial extraction
Uncertain	0.4-0.5	Voice-extracted data awaiting review
Verified	0.6-0.7	Data that passed automated review
Human-approved	0.8-0.95	Data approved by a human reviewer
Authoritative	1.0	Data from authoritative system integrations (direct EHR feeds)

The dashboard also shows confidence breakdown by data source, so you can see which sources produce the most reliable data and which generate the most review queue items. A daily confidence timeseries shows low-confidence and high-confidence event counts over time, making it easy to spot trends after configuration changes.

Review pipeline metrics track how the automated and human review stages are performing:

• Auto-approved - Events that passed automated review without human involvement
• Auto-verified - Events verified by the automated review judge
• Rejected - Events that failed review
• Pending review - Events waiting in the human review queue
• Human-approved - Events approved by an operator
• Corrected - Events where an operator provided corrected data
• Review rate - Percentage of events that required any form of review

Pipeline Health

Pipeline health metrics provide real-time visibility into the connector runner's operational state. These are the metrics you check when something feels wrong - data is stale, surfaces are not getting filled, or outbound sync is backed up.

• Overall status - Healthy, degraded, or starting - with active poll count and total event/entity counts
• Per-source connection health - Whether each data source is reachable and polling successfully, with last poll time, duration, and event counts
• Loop states - Current state of each background process (entity resolution, review, outbound sync, reconciliation)
• Outbound sync status - Per-sink breakdown of synced, failed, and pending events
• Throughput time series - Event ingestion over time, bucketed by hour or day, filterable by source

Sources are automatically marked unhealthy after consecutive poll failures and recover when polling succeeds again. The dashboard degrades gracefully - if the connector runner is temporarily unavailable, stored metrics (event counts, sync history, review stats) remain available without live loop status.

Additional pipeline detail is available per source:

• Entity resolution metrics - Total merges, recent merge activity, and resolution loop status
• Review pipeline metrics - Queue depth, pending items by priority, approval/rejection counts, and average review time
• Outbound sync detail - Per-sink event counts, failure reasons, and retry status

Platform Health Monitoring

Two dedicated observability views provide real-time visibility into data pipeline health:

• Connector health - per-source event health for every data source writing into the workspace. Each source entry includes events ingested in the last hour and last 24 hours, mean events per minute, the last ingested timestamp, and a freshness category (fresh, stale, quiet, or never). Operators can spot a stalled connector at a glance.
• Loop latency - measures end-to-end time from data ingestion (a world model event landing) to agent action (the agent acting on that data). Returns an overall median latency, total pair count, and an hourly sparkline (up to 7 days) with per-hour pair counts and median latency. This is the metric that tells you whether the data-to-action loop is fast enough for real-time clinical workflows.

These pair with the pipeline health metrics above. Pipeline health tells you whether connectors are running. Platform health monitoring tells you whether the data they produce is reaching agents in time.

Command Center Dashboard

The command center provides a single-pane workspace health view that aggregates metrics from across the platform into four sections. This is the "is everything OK?" dashboard - one screen that tells an operations team whether voice calls are flowing, data pipelines are healthy, data quality is acceptable, and identity systems are functioning.

Section	Key Metrics
Voice	Active calls, escalated calls, calls today, average quality score, escalation rate
Pipeline	Source health counts (healthy/degraded/failing), events last hour, outbound pending/failed
Data Quality	Pending reviews, 7-day approval rate, average confidence, total entities, recent merges
Identity	Active API keys, active sessions, failed auth attempts, locked accounts, MFA coverage

Each section fails independently - if one data source is unavailable, the other sections still return results with a degraded indicator. The response includes a list of degraded sections so dashboards can show partial data with appropriate warnings.

Alerts are derived from threshold checks on the aggregated metrics: escalation rate above threshold, failing data sources, low approval rate, high outbound failure count. This gives operations teams a single view to answer "is anything broken right now?" without drilling into individual dashboards.

Production Health Monitoring

The Developer Console provides workspace-level and per-agent production health monitoring. The workspace home page displays a fleet overview grid where each agent card shows its current production status (Live, Degraded, or Paused), pass rate, escalation rate, and recent call volume. A workspace pulse strip above the grid summarizes active agents, call volume, active calls, average quality, escalation rate, and tool success rate with period-over-period deltas.

Each agent links to a dedicated production detail page with KPI cards, trend charts for call quality, latency, caller emotion distribution, and tool performance, and a filterable recent calls list. A period selector lets you view 24-hour, 7-day, or 30-day windows.

Production status is computed automatically:

• Live - The agent is deployed, receiving traffic, and metrics are within healthy thresholds.
• Degraded - The pass rate has dropped below 80, the escalation rate exceeds twice the baseline for the equivalent prior period, or the agent received zero calls in the selected window.
• Paused - The agent deployment is inactive.

The workspace home page also includes a pipeline and data quality section showing ingest source health, event throughput, outbound sync queue status, entity resolution confidence, deduplication activity, and review backlog.

Entity Intelligence

The world model stores entities (patients, providers, appointments, medications) and their relationships. Four capabilities provide visibility into this entity data:

Capability	What It Shows
Relationship graph	One-level graph of all edges (same_as, related_to) from an entity, with connected entity metadata
Data provenance	Full lineage for an entity - contributing data sources, confidence history, merge events
Duplicate detection	Suspected duplicates sorted by confidence, filterable by entity type
Entity search	Search entities by name with filters for type, source, and minimum confidence

These tools let operations teams audit how the world model arrived at a particular entity state and catch duplicate records before they cause downstream issues.

Entity Narrative Briefs

Entity narrative briefs are AI-generated summaries that synthesize a patient's world model data into a structured narrative. The platform reads the last 90 days of events for a patient, distills them into a Markdown narrative and a structured JSON representation, and persists the result as a world model event for fast retrieval.

Each brief includes:

• Evidence pointers - the event IDs that informed the narrative, so every claim in the brief is traceable back to source data
• Confidence score - a 0.0-1.0 score reflecting the reliability of the underlying data
• Event count - how many events went into the synthesis
• Prompt version - the version of the generation prompt used, for traceability as the summarization logic evolves

Briefs are cached after generation. Cache misses fall through to the event store transparently. When no brief has been generated yet, the API returns a consistent empty shape rather than an error, so UIs can render a "not yet generated" state without special error handling.

Briefs are scoped to patient entities today. Cohort, territory, and workspace-level briefs are planned for a future release.

Relationship Graph

The relationship graph shows one level of connections from any entity. Each edge carries a relationship type (same_as for duplicates, related_to for associations like patient-to-provider) and the connected entity's metadata. This is useful for understanding how entities relate to each other and for verifying that entity resolution has correctly linked records.

Data Provenance

Data provenance traces the full lineage of an entity: which data sources contributed, how confidence changed over time, and which merge events combined records. When a patient record has conflicting information (two different phone numbers from two different sources), provenance shows exactly where each value came from and why the current value was chosen.

Duplicate Detection

Duplicate detection surfaces suspected duplicate entities - records that the entity resolution system has flagged as likely referring to the same real-world person or object. Results are sorted by confidence and filterable by entity type, so operations teams can prioritize high-confidence duplicates for review first.

Entity search lets you find entities by name with filters for type, source, and minimum confidence. This is the starting point for most investigations - find the entity, then use the relationship graph and provenance tools to understand its state.

Population Health Analytics

The platform provides population health analytics surfaces that visualize patient risk at both individual and district levels. These surfaces include:

• Patient topology - scatter plot visualization of patient populations with predicted risk scores, cluster assignments, and demographic attributes. Useful for identifying high-risk cohorts and emerging patterns.
• District-level metrics - per-district observed vs predicted disease incidence, primary care capacity gaps, and unmet demand scoring for resource allocation decisions.
• Anomaly alerts - emerging health anomalies with causal decomposition, recommended actions, and projections of what happens if no action is taken. Alerts are prioritized by severity and include supporting statistical context.
• Forecast fans - time-series forecasts with confidence intervals across multiple scenarios (baseline, with policy intervention, observational). Used for both observational monitoring and intervention planning.
• Positive signals - headline metrics highlighting positive trends across the population, providing a balanced view alongside alerts and anomalies.

All population health data is workspace-scoped and accessible through the Platform API.

Dashboards

The platform includes built-in dashboards that visualize key operational and evaluation data. These dashboards are pre-configured and available to every workspace without additional setup.

• Data Browser Insights - Overview of workspace data activity, including record volumes and data freshness.
• Metrics Overview - Summary of active metrics, recent scores, and trend lines across all configured metrics.
• Eval Summary - Workspace-level simulation and eval metric scores broken down by metric, over time, and by categorical outcome. The eval summary dashboard reads from the durable analytical store and includes panels for average eval score by metric (horizontal bar chart), daily average eval score trend (time series), and categorical eval outcome distributions (stacked bar chart). Filters let you scope the view by time window, metric source (simulation, production, or all), and metric type (numerical, categorical, boolean, or all). A summary row shows the number of distinct eval metrics, total value points, scored events, average confidence, and the timestamp of the most recent computation.

Dashboards refresh automatically at a regular interval and support interactive filtering. Each dashboard is identified by a stable slug and is accessible from the analytics section of the Developer Console.

Every workspace also ships with pre-built metric dashboards covering voice quality trends, surface completion rates, data quality distribution, and cross-channel engagement - the operational views most deployments need from day one. These default dashboards use the same embeddable component as custom dashboards, so they can be extended, customized, or embedded in external tools. The Realtime Metric Store dashboard is described in Metric Store.

Embeddable Dashboards

Analytics dashboards can be embedded outside the Developer Console using a reusable dashboard component. Dashboards are workspace-scoped resources with a slug identifier, title, and description. The platform serves dashboard definitions (chart configurations, data queries) through the API, and the embeddable component renders them with interactive charting.

This supports two use cases:

• Developer Console integration - the console's built-in dashboard pages render the same embeddable component, so custom dashboards appear alongside the standard analytics views
• External embedding - the same component can be embedded in customer-facing portals or internal tools, authenticated through either API tokens or backend-for-frontend proxy authentication

Dashboards are managed through the platform API (create, list, get by slug). Each dashboard definition specifies its chart type, data source query, and layout. The rendering component handles data fetching, charting, and responsive layout independently.

Insights Agent

The Insights Agent is a conversational interface for exploring workspace data. Rather than navigating dashboards and filtering tables, operators ask questions in natural language and receive structured analysis with visualizations.

The agent streams responses in real time - reasoning steps, tool invocations, and data queries are visible as they execute, so operators can follow the analysis as it unfolds. When the agent queries the platform's analytics endpoints or metric store, the results appear inline as formatted tables and charts.

Typical queries:

• "How did call quality change after we updated the scheduling context graph last Tuesday?"
• "Which surface fields have the highest abandonment rates this month?"
• "Show me the calls that scored below 60 this week and what went wrong"
• "Compare escalation rates between the downtown and suburban locations"

The Insights Agent uses the same tool infrastructure as the voice and text agents - it calls the workspace's analytics and metric store endpoints to answer questions, so results always reflect live data rather than cached summaries. Visualizations use a full charting grammar - bar charts, line charts, heatmaps, scatter plots, annotations, and subplots - rendered inline alongside the analysis. The same charting format powers the platform's embeddable dashboards, so insights visualizations are consistent with dashboard views.

Developer Guide - For the full analytics endpoint set, see Analytics and Insights in the developer guide.