Source signals
- Pages
- Companies
- Users
- Visits
AI insights
Turn product signals into clear next steps.
AI Insights connects Pages, Companies, Users, and Visits to explain what changed, why it matters, who is affected, and what your team should review next.
From product signals to clear next steps.
Explore a sample SaaS project. No credit card required.
Explanation layer
Every insight starts with product evidence.
AI Insights is the explanation and recommendation layer of Hymetry. Pages, Companies, Users, and Visits surface product signals. AI Insights helps teams understand what those signals mean and what to do next.
Hymetry already structures product behavior across pages, accounts, users, and sessions. AI Insights uses that structured evidence to summarize important changes, explain why they matter, and suggest a practical next step.
Metrics show that something changed. AI Insights helps teams understand what changed, where to look, and what to do next.
Evidence
- Adoption changes
- Engagement shifts
- Peer gaps
- Friction moments
- Session context
AI Insights
- Explanation
- Affected entities
- Supporting evidence
- Recommended action
- Source links
Team action
- Prioritize a fix
- Help an account
- Validate a workflow
- Review a session
- Expand adoption
Product questions
Get from dashboard signal to decision faster.
AI Insights is designed for the moments when a team sees a metric change but needs help turning it into a useful investigation.
What changed?
Summarize changes in adoption, engagement, users, accounts, product areas, and sessions.
Why does it matter?
Explain the impact in plain language, with the account, user, page, or workflow affected.
What evidence supports it?
Show the metrics, comparisons, trends, and session context behind each recommendation.
What should we review next?
Suggest practical next steps for product, success, account, or research teams.
A useful insight is more than a summary.
Each insight should show what changed, who is affected, why it matters, what evidence supports it, and what the team should do next.
Edgewater Labs
Account riskMedium confidence
Usage has softened and several users may need attention.
Edgewater Labs is still active, but recent usage is concentrated in two product areas and several users dropped below their normal activity level.
18 at-risk users2 adopted areasUsage softened vs similar accountsSeveral users below normal activity
Recommended actionCheck at-risk users and add backup champions.Open users table
Insights across the full product model.
AI Insights can summarize different kinds of product signals depending on where the evidence appears: page adoption, account health, user behavior, or session context.
Product adoption
Explain which pages, product areas, or workflows are gaining, losing, or missing adoption.
Integration detail is growing, while Reporting remains unused in engaged accounts.Account and user health
Connect account risk, expansion readiness, user momentum, and attention signals to the people affected.
Usage softened for 18 users, but the account still has strong active-user depth.Session context
Use visits and replay context when a recommendation needs proof beyond aggregate metrics.
Dense activity before a workflow shift can be opened in the source session.Explain account risk and expansion signals.
Companies surfaces account health, adoption breadth, risk, reactivation, and expansion opportunities. AI Insights turns those signals into plain-language recommendations that success and account teams can review.
Usage has softened and several users may need attention.
Recent usage is active but fragile: health shifted toward lighter behavior and adoption is concentrated in a small part of the product.
18 at-risk users
User health shifted toward light and passive usage
Adoption remains concentrated in 2 product areas
Recommended action
Check at-risk users.
Account engagement is strong, but valuable workflows are not adopted yet.
The account has enough active usage to support expansion, but the Reporting workflow has not become part of the team's routine.
High active users
No Reporting adoption
Healthy interaction rate
Recommended action
Introduce Reporting workflow.
Explain individual user behavior without losing account context.
Users surfaces engagement, consistency, intensity, page usage, peer comparison, and momentum. AI Insights helps teams understand whether a person is a champion, slipping user, unusual workflow, or onboarding opportunity.
Susan Eriksson · HelixBio · Unusual usage
This user is highly engaged and unusually weighted toward Project management compared with company peers.
- Top 1% by engaged time among peers
- +33pp Project management share vs peer median
- 23m engaged, +616%
- 100% interaction rate
Recommended action: Validate Project management usage.
Every recommendation should show its evidence.
AI Insights should not ask teams to trust a black box. Each recommendation should make the supporting metrics, comparisons, and source views visible.
Insight
Usage has softened and several users may need attention.
The recommendation stays tied to the source views behind it, so a team can inspect the account, users, peer comparison, pages, and visits before deciding what to do.
- Company metrics show that the account is still active.
- User health explains where engagement is becoming fragile.
- Pages and visits point to the source context behind the recommendation.
Recommended action: Check at-risk users and add backup champions.
Evidence behind this insight
Company metrics
Active users, engaged time, and adoption breadth show whether the account is still healthy.
42Active companies
68%Adoption breadth
-12%vs last 30 days
User health
Light, passive, dropped, and at-risk users explain where engagement is becoming fragile.
1,284 Healthy
326 Passive
118 At risk
72 Dropped
Peer comparison
Similar companies and median behavior make it easier to separate noise from meaningful change.
Your performanceAbove medianTop 25%
Engaged time vs peers
+18%
Pages used
Product areas and pages adopted show whether usage is broad or concentrated.
128Pages used
86%Core area adoption
Core product 62%
Admin 18%
Billing 12%
Other 8%
Visits
Replay context helps confirm what happened when a signal needs deeper investigation.
8,732Sessions
12m 46sAvg. engaged time
Use replay context when a signal needs proof.
Some insights are easier to understand when the team can inspect a session. Visits provides the page path, event timing, replay controls, and friction context behind a recommendation.
Connect the same signal across every product view.
A good insight should travel across Hymetry. A page signal can lead to affected companies, users, and sessions. An account signal can lead to the pages and people behind it.
| Signal | Pages | Companies | Users | Visits | AI output |
|---|---|---|---|---|---|
| Page adoption changed | Dashboard adoption 92% | Low-interaction companies | Page champions | Replay sessions | Review page usage quality |
| Account risk increased | Narrow area usage | Edgewater Labs | 18 at-risk users | Recent sessions | Check backup champions |
| User behavior changed | Project management pages | HelixBio | Susan Eriksson | Session paths | Validate unusual workflow |
| Session friction appeared | Work Packages | Affected accounts | Session actor | Dense event cluster | Review possible friction |
Recommendations with guardrails.
AI Insights should help teams focus, not replace judgment. Each recommendation should be grounded in evidence, show confidence, and make it easy to inspect the source.
Evidence first
Show the metrics, deltas, comparisons, and sessions behind the insight.
Confidence, not certainty
Use confidence labels and careful language when evidence is limited or directional.
Human review
Keep recommendations as next steps for teams to review, not automatic decisions.
AI should summarize and prioritize; your team still decides what to ship, fix, or communicate.
AI Insights connects the rest of Hymetry.
Turn product signals into action.
Explore a demo project first, then create your workspace when you’re ready.
No credit card required.