Guide Completion Rate
0%
vs. 23% industry avg
+308%
Time to First Deploy
0 hrs
from 72hr avg
−94%
Steps Failed Per Guide
0
avg failure events
vs 8.1 industry
Support Tickets / Onboard
0
down from 14.7
−92%
Docs Abandonment Rate
0%
industry: 77%
−91%
Guides Active Today
0
live executions
↑ 12% this week
DIAGNOSTIC REPORT // 2026-02-27

Your implementation docs have a 77%
abandonment rate.

Engineers open the Confluence page, hit step 3, get confused, and Slack someone. That person is you. Deploy structures execution paths that run clean — on the first try, at 2 AM, without you in the loop.

LIVE EXECUTIONS
--- guides running now
████ SECTION 01
THE TEARDOWN

Traditional docs fail by design.

Confluence pages. Scattered READMEs. Tribal knowledge locked in Slack threads. Every metric below is measurable, reproducible, and fixable. Here's the delta.

Metric
Traditional Docs(Confluence / README)
Deploy Guides
Guide Adoption Rateof engineers complete
23%
94%
Avg Onboarding Timeto first successful deploy
72 hrs
4.2 hrs
Error Frequencyavg step failures
8.1 failures/guide
0.3 failures/guide
Support Tickets Generatedtickets per new engineer
14.7 / onboard
1.2 / onboard
Documentation Rot (6mo)of guides outdated
68% stale
4% stale
Time to Locate Correct Docsearch + context switch
23 min avg
< 90 sec
Migration Success Ratefirst-attempt success
41%
96%
⚡ COST OF INACTION

A 10-engineer team onboarding quarterly loses ~2,880 engineering hours/year to documentation failure. That's 1.4 senior engineers.

████ SECTION 02
ARCHITECTURE

How guides actually get followed.

STRUCTURE

Every guide is an executable tree, not a document

Guides in Deploy are structured as dependency graphs — each step knows its prerequisites, its outputs, and its failure modes. No ambiguity about order. No "check the previous section" footnotes.

  • Prerequisite validation before step 1 runs
  • Output artifacts passed between steps automatically
  • Failure state defined for every step, not just the happy path
CONDITIONAL LOGIC

Edge cases are first-class citizens, not footnotes

Your production deploy has different requirements than staging. Your AWS setup differs from GCP. Deploy handles branching logic inline — the engineer sees only the path relevant to their context.

  • Environment-aware branching (prod vs staging vs dev)
  • Platform conditionals (AWS / GCP / Azure / on-prem)
  • Team-size variants (solo engineer vs 12-person squad)
PROGRESS TRACKING

Blockers surface before they cascade into incidents

Real-time execution state means you see exactly where a team member is stuck — not after they've created 14 support tickets, but at step 3, before the blast radius widens.

  • Live step completion across all active guides
  • Blocker detection: step stalled > threshold triggers alert
  • Aggregate metrics: which steps fail most across the org
████ SECTION 03
LIVE PREVIEW

Click through a real execution.

This is what your engineer sees at 2 AM. Click each step. Watch it run. No ambiguity. No "see also." Just execution.

deploy_k8s_production.guide
0/3 steps
01
Validate cluster connectivity
$ kubectl cluster-info --context prod-us-east-1
→ Click to execute
02
Build and push container image
$ docker buildx build --platform linux/amd64 -t registry.example.com/api:$COMMIT_SHA --push .
03
Apply Kubernetes manifests
$ kubectl apply -f k8s/production/ --record
OUTPUT
$ _
// Click a step to execute
████ SECTION 04
BENCHMARK

Run the benchmark.

Three questions. Instant personalized comparison. No email required.

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Complete the diagnostic to see your personalized comparison