Connect the org
GitHub, repos, and agent surfaces. CLI, MCP, IDE, connect once.
Engineering productivity metrics
See the work Git can't.
One view of the hours, agent runs, and token spend behind everything your engineers shipped this week.
Definition
Team coding time visibility means knowing how many hours each engineer and each AI agent spent on a repo or project, and what that time produced. DevClocked builds it from a git baseline plus telemetry from a lightweight editor extension and an editor-agnostic CLI tracker, so terminal work and Claude Code, Codex CLI, and Cursor sessions are counted alongside commits and pull requests. Teams get hours per project, the AI-versus-human split, token cost, and a Leverage Score without anyone starting a timer.
One org view
Every team, every agent, every dollar, in one view.
Human hours, agent runs, token spend, and shipped output side by side, per team and for the whole org, captured at the source, no manual input.
Org leverage
This month, every team in one view.
TeamHrsRunsTokensLev
Checkout312h1,840$2,1404.1x
Platform198h920$1,0703.2x
Growth144h2,310$1,8805.0x
Org654h5,070$5,0903.8x
Org time slice
Feature coding41%
Config & toil37%
Review & debug22%
25 features + infra shipped across 3 teams. Captured at the source, no manual input.
The blind spot for leaders
Your AI bill is growing. Your visibility isn't.
Git only sees what landed. The AI invoice is a black box. And when six engineers and a stack of agents touch one service, who-shipped-what becomes a guess. DevClocked breaks it down per person, so contribution, hours, and agent spend are no longer a mystery.
Checkout team
Contribution this sprint, per person.
PersonTimeAgent $Ships
PRPriya R.Staff eng
42h$6129MLMarcus L.Senior
34h$3886SOSam O.Senior
20h$4205DKDana K.Mid
22h$1744Team118h$1,59424
Output, hours, and agent spend per person, captured at the source. No standups required.
One work layer, three owners
Three buyers, three reasons to believe.
One source-level work record, regrouped around the person who has to act on it.
For engineering leadership
Team and org Leverage Score, output per engineer, human-vs-agent mix, and trend lines that show where the multiplier is real, and where it isn’t.
For platform / EM
Attribution across humans, agents, teams, and repos. See who and what shipped each feature without chasing standups or stitching logs.
For finance / FinOps
Token cost by team, project, and agent mapped to delivered output. The AI bill finally connects to what it produced, defensible at budget time.
No agents-in-the-loop, no timesheets
Captured at the source. Nothing for your engineers to do.
Step 1
Step 2
Engineers work normally
Zero-touch capture. No timers, no screenshots, no behaviour change.
Step 3
Leaders get the operating layer
Dashboards, attribution, and AI-spend-to-output update automatically.
Is the AI spend paying off?
Tie every dollar of token spend to what it shipped.
$5,090 in agent tokens last month bought 25 features plus infra across three teams, at 3.8x org leverage. The AI invoice stops being a black box and starts being a return.
Spend to output
A worked example, reconciled automatically.
Agent token spend
$5,090
across 3 teams, last month
Shipped
25 features + infra
tied to the work blocks that produced it
Org leverage
3.8x
output per human hour, human + agent
Built for how orgs buy
Output, not surveillance, with the controls enterprise needs.
Privacy-first by design, with the governance an engineering org has to clear before it rolls anything out.
Role-based access
Team and project roles with per-member sharing controls, so the right people see the right org, team, and project views.
Output, not surveillance
No screenshots, no keystroke logging, no idle-time spying. We measure what shipped, not whether someone was watched.
Retention, audit & export
Tier-based retention, admin audit visibility, and clean export and deletion, so the work record is yours to govern and move.
Planned
SSO / SAML & residency
Enterprise single sign-on, SAML/SCIM, and control over where org data lives are coming next on the Business and Enterprise path.
How it compares
Git tools see the commit. Token tools see the bill. We see the work.
The short version of the buyer FAQ, what each category can and can't show you.
Git-analytics
Waydev, Jellyfish, LinearB
Token tools
Tokscale, Langfuse
DevClocked
Sees work that never commits
Human + agent in one view
partial
Spend tied to shipped output
Captured at the source
scrapes commits
No surveillance
varies
n/a
FAQ
Questions buyers ask
The short answers for teams comparing Git analytics, token counters, and agent observability.
For orgs
Business tier for teams adopting agents at scale.
Org dashboard, cross-team attribution, governance, and benchmarked leverage, the operating layer for engineering leaders, platform teams, and finance.
Business cohort
Make AI leverage measurable.
Bring humans, agents, repos, and shipped output into one source-level picture.