xeve started as a personal analytics tool. You install the tracker, connect your integrations, and see your own data. But companies asked: can we deploy this to our team and get organizational insights?
Today, we are launching xeve Enterprise — role-based admin dashboards with AI-generated narrative insights. Every executive gets the dashboard they actually need, not a one-size-fits-all analytics page.
The setup takes 5 minutes:
The key design principle: zero changes to the tracker. The enterprise layer is purely additive — new tables, new aggregation, new routes. Employees' existing data gets aggregated up through org membership. If an employee leaves, their personal data stays; org aggregates use pre-computed summaries.
Each admin role gets a purpose-built dashboard. Here is what each one shows — and a sample of the AI insights they receive.
The CEO dashboard opens with a single number: the Company Health Score (0-100). This combines employee engagement rate, median productivity, coding output per headcount, and meeting overhead into one signal. Below it: department comparison charts, headcount efficiency metrics, and risk indicators (burnout signals, declining engagement, excessive meeting cultures).
Here is a sample of the weekly AI strategic insight a CEO receives:
Company Health Score: 74/100
This week's health score reflects strong engineering output offset by concerning meeting overhead trends. Your active employee rate held steady at 87%, and median productivity rose to 62% — both healthy. However, total meeting time increased 18% week-over-week, driven primarily by the Product and Design departments.
Department Rankings:
- Engineering: 6.2h avg productive/day, 847 commits, 43 PRs merged — highest output per headcount
- Design: 5.1h avg productive/day, strong Figma time but 38% of tracked time in meetings
- Marketing: 4.3h avg productive/day — lowest, but output is content-heavy and harder to measure
- Product: 3.8h avg productive/day — 52% meeting time is a red flag
Recommendations:
- Product department meetings need an audit. At 52% meeting time, their team has less than 4 productive hours per day. Consider async standups and reducing recurring meeting cadence.
- Engineering is performing exceptionally. The commit velocity increase of 23% suggests the recent hiring is paying off. Protect their focus time — their meeting load is currently 14%, keep it below 20%.
- Cross-department collaboration between Engineering and Design shows high tool overlap (87% shared categories). Consider co-located sprints or shared channels to reduce handoff overhead.
The CTO dashboard leads with four velocity metrics: total coding time, commits, PRs merged, and net code growth. Below: a sortable developer productivity table, project progress bars linked to GitHub repos, and focus vs meeting analysis per developer.
Sample CTO weekly insight:
Engineering Health Score: 81/100
Strong week for velocity. The team shipped 847 commits across 12 repositories with 43 PRs merged (51 opened — 84% merge rate). Total coding time was 312h across 23 active developers, averaging 13.6h/developer/week.
Developer Focus Analysis:
- Sarah Chen leads with 28.3h coding and 67 commits. Her meeting load is only 8% — protect this.
- James Rodriguez has 4.2h coding but 11.8h in meetings (74% meeting time). His PR reviews are fast (avg 2.1h turnaround) but he has no deep work blocks.
- Two developers (Alex Kim, Priya Patel) show declining velocity: -34% and -28% respectively. Both joined the platform project 2 weeks ago — may be ramping up on unfamiliar code.
Tech Debt Signal: The
api-gatewayrepo has 73% more lines removed than added this week. This is either a healthy cleanup or a concerning rewrite. Worth a check-in with the team lead.Recommendations:
- Shield James Rodriguez from meetings. His technical judgment is valuable in reviews, but 74% meeting time means zero deep work. Move him to async reviews for 2 weeks as an experiment.
- The platform project ramp-up is expected to take 2-3 weeks. Monitor Alex and Priya's velocity next week — if still declining, pair them with a senior on the project.
The CFO dashboard focuses on output per dollar. Cost efficiency score, tool utilization (licensed tools vs actual usage), meeting overhead analysis, and department cost-per-output rankings.
Sample CFO insight:
Cost Efficiency Score: 68/100
Your organization's utilization rate is 87% (43/50 active employees). Per-employee productive output averages 5.1h/day, which is above the 4.5h industry benchmark for knowledge workers.
Tool Spend Optimization:
- Figma: 8 licensed seats, 6 active users (75% utilization) — acceptable
- Jira: 50 seats, 12 active users (24% utilization) — significant waste. Consider downgrading plan or consolidating onto Linear.
- Slack: Universal adoption, 100% utilization — but it is the #1 distraction source at 1.8h/person/day average. Consider Slack-free focus hours.
- GitHub Copilot: 18 seats, 18 active — good ROI signal, developers using it average 23% more coding time.
Meeting Overhead: 28% of total tracked time is in meetings. At an average loaded cost of $85/hour, the weekly meeting overhead is approximately $47,600. Reducing to 20% would recoup ~$13,600/week in productive capacity.
Recommendations:
- Audit Jira licenses. At 24% utilization, you are paying for 38 unused seats. Potential savings: $7,600/year.
- Implement company-wide "No Meeting Wednesday." Based on current data, this could recover 850 productive hours per month across the org.
The COO dashboard visualizes how teams work together. A cross-department collaboration heatmap, bottleneck detection (departments with high meeting:output ratios), communication pattern analysis, and operational efficiency scoring.
Sample COO insight:
Operational Efficiency Score: 71/100
Bottleneck Report:
- Product department: 52% meeting time, 3.8h productive/day — critical bottleneck. This team is in meetings more than they are working. Root cause appears to be 6+ recurring weekly syncs.
- Design → Engineering handoff: 3.2-day average lag between Figma activity ending and related GitHub commits starting. Consider design review earlier in the sprint cycle.
Cross-Team Collaboration: Engineering and QA show 91% tool overlap — strong collaboration. Marketing and Product show only 34% overlap — potential silo. Sales and Engineering: 12% overlap, which is expected and healthy.
Recommendations:
- Reduce Product department recurring meetings from 6 to 3 per week. Replace the others with async Loom updates.
- Create a shared Slack channel or weekly sync between Marketing and Product to close the 34% collaboration gap.
The marketing dashboard tracks creative tool usage (Figma, Canva, Adobe suite), campaign sprint progress, team velocity, and content production metrics.
Sample Marketing Head insight:
Team Velocity Score: 77/100
The creative team logged 89h in design tools this week — up 12% from last week. Figma dominated at 52h, followed by Canva (21h) and Adobe Illustrator (16h). Average creative output per team member is 11.1h/week in design tools, which is strong.
Campaign Sprint Status:
- Q1 Brand Refresh: 78% timeline elapsed, strong Figma activity — on track
- Product Launch Campaign: 45% timeline, but design activity dropped 30% this week — may need attention
Recommendations:
- The Product Launch Campaign needs a velocity check. Design activity dropped while the deadline approaches. Schedule a sprint review this week.
- Consider consolidating Canva and Adobe Illustrator workflows. Two team members are using both for similar outputs — standardize on one tool to reduce context switching.
The enterprise layer enforces strict data boundaries. Admins never see window titles, file paths, individual app names, music, or locations. They see category-level aggregations, coding totals, and GitHub summaries. Health data is opt-in only — employees must explicitly enable sharing.
This is enforced at the database level via PostgreSQL SECURITY DEFINER functions. Admin dashboards call RPC functions that aggregate data internally — admins never query individual employee rows directly. The privacy model is not a UI constraint; it is a database constraint.
Employees keep their full personal dashboard — all 24 pages work exactly as before. The only addition is a small org badge in their sidebar linking to the org. In their settings, a new "Organization" section shows exactly what data is shared with admins, with toggles for each data category.
The Mac tracker requires zero changes. Employees download the same app, sign in, and their data flows automatically. If the org has set track_window_titles: false, the tracker strips window titles before upload — this is the only org-specific behavior.
Enterprise trial is free. Head to xeve.io/org/new, create your organization in 5 minutes, and invite your team. AI insights generate weekly once you have a few days of data.
For questions or a guided onboarding, reach out — we will help you get set up.