

BACKED BY STANFORD & DRAPER
XTrace captures and surfaces the right context as work happens, so every team and agent in your org can build on it.
−82%
lower cost on work your team has already done once
63%
of sessions start from what the team already worked out
3 wks → 3 hrs
for a new hire to onboard because the agent has past context
Day 1
new people work the way your best person works
See, learn, do. It’s that simple.
Your team’s work flows in. What worked gets kept and mistakes learned. What’s learned becomes something any person or AI can use.
Prove the ROI of AI-assisted engineering.
Connect agent activity to shipped work. Measure output, human intervention, token cost, and hands-off time across every organization, repository, and ticket type.
More output per engineer
Track merged PRs per engineer and see whether AI is materially increasing engineering throughput.
Less human intervention
Measure human messages and hands-off time per merged PR to find workflows that still need supervision.
Lower cost per shipped change
Compare tokens per merged PR across features, fixes, chores, and teams—not token spend without an outcome.
Engineering ROI
LAST 7 DAYS
22.8
merged PRs / engineer · +11%
31
human messages / feature PR
2.3 min
hands-off time · −7%
EFFICIENCY BY TICKET TYPE
Feature PRs
594.6M tokens · 31 msgs
Fix PRs
418.3M tokens · 60 msgs
Chore PRs
323.9M tokens · 21 msgs
Fix PRs require nearly 2× more human messages than feature PRs. Standardize the winning workflow as a skill.
VISIBILITY ✓
→
ENFORCEMENT
RULEBOOK ENFORCEMENT
01
Protect engineering capacity
Stop repeat mistakes before they consume review cycles, senior attention, and delivery time.
02
Scale your best judgment
Turn team standards into linters, hooks, and judges every agent follows.
03
Show the business impact
Connect every intervention to avoided rework, lower risk, and hours returned to the roadmap.
Rulebook
14-day impact
38h
ENGINEERING TIME SAVED
1,561
RULE FIRED
−27%
REPEAT VIOLATIONS
Where value is protected
22 ACTIVE CONTROLS
Verification before completion
STOP HOOK
Blocks done until required checks pass · 12 interventions · 42m average review loop saved
Reuse existing architecture
AGENT JUDGE
Redirects agents to proven systems · 8 assists · parallel implementations avoided
NEXT BEST CONTROL
Protect client-side security boundaries
Built for teams that run on AI.

Six engineers, six agents, six different answers to the same question.
Every Claude Code session starts cold. The spec someone wrote six weeks ago sits in a doc nobody reopens, so the code drifts away from it quietly and you only catch it in review, or in production.
One shared repo brain holds the specs, the decisions, the reviews and every past session. New sessions start from what the team already decided, and a routine checks each pull request against the spec that covers those files.
$/memhub:spec check
Spec: Retry policyv4· covers app/retry.py, app/**/backoff.py
! app/retry.py changed 3× since v4 · wait cap is now 30s, spec says 5s
→ suggest a change, or update the spec


