Hello Alex Docs

AI Intelligence Overview

Analyze conversion, quality, and coaching signals across pathways. Read the AI Intelligence guide in Hello Alex documentation.

Overview

AI Intelligence helps teams improve outcomes because it translates call behavior into prioritized coaching and optimization signals.

Aggregated trends surface shifts in quality, conversion, and transfer performance over time.
Pathway- and call-level drill-downs connect signal changes to concrete examples, enabling targeted edits rather than broad guesswork.
Generate Analysis runs a fresh AI pass over recent calls, while Auto Generate Analysis queues analysis for each newly completed call after you confirm billing.
From Tags turns Call History transcript tags into pathway fix suggestions you can review and apply from the same drawer used on Insights.

Good to know: insight review is most effective when tied to a recurring optimization cycle after launches.

Overview consolidates top signals so teams can prioritize improvement work quickly.

Open the highest-impact recommendation and trace it to pathway-level contributors.

Headline recommendations summarize where outcomes shifted, reducing time spent on manual report assembly.
Trend movement highlights whether recent launches or pathway edits produced meaningful changes.
Avg Score: Aggregate quality signal.
Good to know: reviewing overview after each major campaign provides faster feedback loops.
Executive snapshot reporting
Weekly optimization planning

Pathway insights tie performance changes to specific conversation designs.

Implement one suggested improvement and compare week-over-week metric movement.

Per-pathway metrics show booking or conversion movement over comparable periods.
Suggested copy or structure changes provide practical hypotheses for improving weak steps.
Booking rate: Share of calls reaching goal.
Good to know: isolated A/B edits on opening lines often reveal high-leverage gains quickly.
Pathway iteration cycles
Goal-completion improvement

Quality analysis exposes where conversations degrade and where escalation logic needs refinement.

Review low-scoring examples and apply targeted pathway or routing updates.

Dimension-level scoring identifies weak areas such as clarity, transfer accuracy, or completion consistency.
Linked examples in call history make it easier to validate whether observed issues are systemic or isolated.
Transfer accuracy: Warm transfer success rate.
Good to know: pairing quality findings with pathway edits shortens time-to-improvement.
Transfer reliability optimization
Quality postmortem analysis

From Tags converts tagged call moments into pathway improvement suggestions.

Tag a recent problem call, open From Tags, and generate a suggestion for the highest-impact tag.

Tag calls in Call History using AI Error, Needs Correction, Unclear Audio, or Great Response markers.
Non–Great Response tags appear in the From Tags queue where you can click Generate suggestion for an AI pathway fix.
Review and apply accepted suggestions from the same apply drawer used on Insights, then track outcomes under Applied Changes.
Generate suggestion: Requests an AI pathway fix for a tagged moment.
Auto Generate Analysis: When enabled, newly completed calls are analyzed automatically after a billing confirmation.
Good to know: tag at least a handful of calls before relying on From Tags suggestions for pathway edits.
Targeted pathway fixes from real call examples
Coaching loops tied to transcript tags

Pick one underperforming pathway metric and run a focused edit-and-retest loop.