Engineering System Diagnostics

Your delivery pipeline hasstructural bottlenecks.
Samix finds them.

Connect GitHub, PagerDuty, and Linear. Samix analyses PR review patterns, team capacity, service ownership, and code architecture to surface the structural constraints that slow down delivery. It tells you where to investigate and what to fix.

4
Bottleneck patterns
approval, capacity, coupling, latency
6
Data sources
GitHub, PagerDuty, Linear, catalog, feedback, cost
11
Health metrics
with configurable thresholds per org
< 2 min
To first insight
sign up, connect a repo, see results
The Problem

Engineering teams don't fail from lack of effort.They fail from invisible structural constraints.

A 4-person platform team reviewing PRs for 5 other teams. A data engineering squad with 8 repos and 3 engineers. A shared library modified by everyone, owned by no one. These are structural problems — they don't show up in sprint velocity or JIRA burndowns. They show up in 38-hour review queues, 11 incidents a month, and engineers saying "we're drowning" in retros.

Most engineering dashboards track what individuals are doing. Samix tracks what the system is doing to them.

Detection Patterns

Four structural patterns.
Real examples from real codebases.

Centralised Approval

"Platform team is the sole reviewer for 8 repos. 73% of cross-team reviews funnel through a 4-person team."

Signals used

Reviewer concentration, queue depth, avg wait time

Team Capacity

"Data Eng owns 8 repos with 3 engineers. PR backlog grew 40% in 4 weeks. 67% ops ticket ratio."

Signals used

Repos-per-engineer, backlog growth, ops ratio

Architecture Coupling

"shared-lib modified by 5 teams in 30 days. 23 merge conflicts. 68% cross-team PR ratio."

Signals used

Cross-team edits, merge conflicts, contributor count

Review Latency Spike

"payment-gateway P90 review latency jumped from 16h to 72h. Correlated with PCI compliance work."

Signals used

P90 latency delta, baseline comparison, PR volume

Diagnostic Loop

Continuous improvement,
not one-off audits.

01

Detect Signal

Metrics surface anomalies across connected data sources.

02

Hypothesise

The engine identifies likely structural causes.

03

Recommend

Each finding gets an owner, type, and follow-up window.

04

Implement

Teams act on findings. The system tracks progress.

05

Measure

After 2-4 weeks, evaluate whether the metric improved.

Repeat every 2-4 weeks
Integrations

Connect your tools.
Each one powers specific signals.

GitHub

multi-repo

PRs, reviews, CODEOWNERS, file trees

Powers

Teams, latency, bottlenecks, dependency graph

PagerDuty

Incidents, services, on-call schedules

Powers

Incident load, alert volume, on-call hours

Linear

Issues, teams, cycles

Powers

Ops ratio, work allocation, backlog depth

Cloud Cost

AWS Cost Explorer, GCP Billing

Powers

Monthly spend, AI cost, cloud breakdown

Service Catalog

Service-team-tier mappings

Powers

Ownership, capacity, sprawl metrics

Feedback

Surveys, retros, interviews (CSV)

Powers

Qualitative sentiment, team signals

See your system health in two minutes.

Sign up and explore a fully loaded demo dashboard with 6 teams, 14 services, and pre-detected bottlenecks. Then connect your own repos.