For builders shipping AI automations, whether you vibe-coded it, wired it up in n8n, or wrote it in custom code, for yourself or for clients. We grade it against a 30-point production standard, hand you the exact fix for every gap, then monitor it and alert you the moment it breaks.
Every gap comes back with the exact fix: where it is, why it bites, and how to fix it.
Four steps. Paste it in, get every gap with the exact fix, and we keep it production-grade as it changes.
Code, a Claude conversation, an n8n, Make, or Zapier export, or a live webhook. We normalize all of it into one model. No instrumentation to start.
Checked against 30 ways an automation goes wrong without anyone noticing. A wrong number. Dropped rows. A value pulled from a page that changed overnight. Each gap comes back with the evidence and what it costs you, in your domain.
Every gap ships with a paste-ready fix. Drop it in, re-grade, and the gap closes. We do not just point at the hole, we hand you the patch.
Re-graded as it runs. When something slips it tells you, before a wrong number reaches a customer and your team has already acted on it.
A score does not stop the wrong number from reaching a customer. Every red checkpoint comes back with what it costs you in your domain and a paste-ready fix. Drop it in, re-grade, and watch the point turn green. Or hand it to us and we get it to green for you.
Blank cells are coerced to 0 before averaging, so a missing value reads as a $0 row and quietly drags the whole total down. Nobody sees it until the output is already in front of a customer.
# Treat missing as missing if raw in ("", "N/A", "-"): return None # never 0 avg = mean(drop_nulls(values))
30 checkpoints, grouped into the eight things that decide whether an automation survives contact with production.
Someone built an automation in a weekend with Claude and does not know it is quietly broken. Neither incumbent serves them.
A blank canvas for engineers. You configure everything, and it tells you the service is up. It never tells you the automation is right. It speaks p99 latency, not whether the answer is right.
Opinionated, standard-driven, certify-me, which is the right shape. But Vanta proves security posture. It says nothing about whether the automation you shipped actually works in production.
The correctness and production-readiness layer for AI automations.
Opinionated like Vanta, live like Datadog, but the question is whether the automation is right. We ship the standard. No config, no blank canvas, in your domain, not your CPU.
Closing every gap and keeping it green for 60 days earns a verifiable certificate. Share it with clients and partners if you want proof the automation works. Optional, never the point. The point is that it works.
All 30 checkpoints green. No known way left for it to be silently wrong.
Green for 60 days of real traffic. Green once is not the same as green under load.
Re-graded as it runs. The certificate is revoked the moment a checkpoint slips, so it never vouches for something broken.
Monitoring keeps every automation right as it runs. Hand it to us and we get it to green for you. Or take the cheap first look for nine dollars to see where you stand.
Grade one automation against all 30 checkpoints and get the exact fix for every gap.
Keep the automations your team runs on green. Continuous re-grade, live alerts, and the path to certified.
We get your automation to green for you, then hand it back certified.
Connect or paste it. We surface what would cost you, a wrong number, a dropped row, a stale value, and hand you the fix for each. The same 30 checks we run on every automation we ship.