Aggregate-only reporting
What leaders can learn without seeing anyone’s private emotional data
This page shows the organization view AIForj is built to support: anonymous counts, completion patterns, shift buckets, and rollout surfaces. It is intentionally designed to answer “is this helping?” without answering “who is struggling?”.
This page is currently reading live aggregate metrics from a durable Vercel Blob reporting store.
Aggregate-only reporting integrity
Clinical review
Last reviewed
April 16, 2026
Built for emotional first aid, not diagnosis or crisis care. Read the editorial policy to see how AIForj writes, reviews, and updates content.
Growth dashboard
Privacy-first funnel visibility
These cards use first-party aggregate events only. No raw messages, journal text, provider queries, full URLs, IP addresses, or ad-tech identifiers are included.
Page views
Aggregate marketing page views
Tool starts
Opt-in sensitive events
Tool completions
Completion rate 0%
Share cards
0 share link opens
Checkout starts
0 conversions logged
Newsletter signups
Email stored separately from emotional content
Provider searches
No location/query details stored
Positive shifts
Bucketed, opt-in completions
Top entry pages
Mood-shift buckets
No data yet
Acquisition sources
Monthly active users
Counted through rotating anonymous client ids, not named user accounts.
Completion rate
Share of started tools that reached completion across all anonymous sessions.
Positive shift rate
Share of measured sessions with a positive bucketed pre/post improvement.
Aggregate events
Count of anonymous start and completion events stored for aggregate reporting.
Live aggregate trend view
This shows the level of insight leaders get: starts, completions, and improvement buckets at a team level. No names. No raw session content.
Top anonymous need categories
Most-used tool families
How people arrived
What leaders can see
- tool starts and completions in aggregate
- bucketed mood-shift outcomes at team level
- which categories are being used most
- high-level adoption trends over time
- how people found the toolkit in aggregate
What leaders cannot see
- names, emails, or employee identities inside usage data
- free-text entries from techniques or interventions
- voice data, transcripts, or conversation content
- an individual person’s raw mood history
- a ranked list of “high-risk” employees
Where these numbers come from
The reporting model is derived from the same narrow event design used in AIForj’s privacy page: tool started, tool completed, duration bucket, shift bucket, and a rotating anonymous client id. It is meant to stay useful while remaining intentionally low-resolution.
For the plain-English public version of that boundary, read What AIForj collects.