AI Engineering ROI
Measure AI engineering by trusted autonomous throughput, not token spend.
Current state vs after activating two Trust Packs — Integration and Frontend Critical Flow. Numbers project from the 100-PR replay; your real numbers generate during the private replay. Demo
Review turnaround
−54%2.6d
1.2d
Median PR-open → merged. Compressed by exception-based review.
Cost per PR
−85%$1.20
$0.18
Route Kimi/SLM on stable workflows; reserve Claude for contract reasoning. Saves $39K/yr at current volume.
Autonomy
+14pts65%
79%
Weighted across all workflows. Core infra stays human-only on change.
Review ROI
Deep senior review
100%43%
Auto-approval candidates
0%31%
Reviewer clarifications / PR
5.42.1
Senior hours reclaimed / month116h
Annualized senior-time value$202K
Model cost ROI
Style / basic policy checks80–95%
ClaudeSLM + rules
Ownership classification60–90%
ClaudeMid-tier + policy
Integration reasoning30–60%
ClaudeClaude only when needed
Core infraSafety first
Claude + humanNo auto route
Monthly spend @ 3.2K PRs/mo
$3,845$577
Autonomy ROI
Agent-safe work (weighted)
65%79%
Core infraStays human-only
Next unlockIntegration Trust Pack
Escalations with clear human-ask100%
Trust Packs to reach 79%2
Build vs buy
A platform pod can build a Claude skill. Building the system around it — historical replay, eval harness, policy compiler, model routing, exception taxonomy, failure atlas, ROI dashboard, ongoing eval maintenance — is a 6-to-9-month effort. We deploy the first Trust Pack in 10 working days.
Internal build
6 to 9 months- PR ingestion + history backfill
- Historical replay engine
- Eval harness + golden-set library
- Policy compiler (rule → check + route + replay)
- Model routing layer with per-workflow benchmarks
- Exception taxonomy + escalation flows
- Failure Atlas + Trust Pack catalog
- ROI dashboard tied to senior hours
- Ongoing eval maintenance + drift detection
Plus ongoing eval maintenance, model-drift retraining, and Trust Pack curation. Estimated Platform pod of 4–6.
VibeOps Trust Pack deployment
10 working days- 1Run connector inside your VPC — read-only GitHub token
- 2Ingest 100 historical PRs for the selected workflow
- 3Build workflow-specific Trust Agent + replay benchmark
- 4Calibrate against your senior engineers' actual decisions
- 5Deliver Autonomy Map, Trust Certificates, exception taxonomy
- 6Decide whether to activate Trust Pack in production
First Trust Pack deployed in 10 working days; first Trust Certificates within 48h of replay. No source code leaves your infra. Customer-owned policies; generalized failure patterns improve every Trust Pack across deployments.
From the 100-PR replay · what generated these numbers
100
PRs replayed across 6 repos
81%
agreement with reviewer outcome
87%
recall on reverted PRs
31%
auto-approval candidates surfaced
The promise · for VibeCorp Engineering or any enterprise org
Show me which parts of engineering can safely run on agents.
After NDA: pick one team, pick one workflow, run a 100-PR replay inside your environment. No source code leaves your infra. You leave with an Autonomy Map, exception taxonomy, model routing plan, and senior-review ROI estimate — and a clear answer to build-vs-buy.