Ground Truth Framework · Free
Your model works in testing.
The gap is why it fails everywhere else.
The diagnostic framework I've used on 40+ production AI systems across defense, medical, industrial, and SaaS. Three checks. Free. No account required.
If you're in the middle of this right now — read this before you touch the model again.
You didn't hire wrong.
You didn't build something unfixable.
The contractors weren't incompetent.
The failure mode you're dealing with is invisible from inside the project — by design. Not because your team wasn't good enough. Because the gap between where your model learned and where it runs only becomes visible when someone stands outside the system and looks at both sides at once.
Nobody inside the project can do that.
Your contractors looked at the model. The model was fine. The problem was somewhere they weren't looking.
In 2015, a team at Google documented something the industry has mostly ignored since: model code represents roughly 5% of a mature production ML system. The other 95% is infrastructure, pipelines, and the boundary between training and deployment.
Every contractor you hired spent their time in the 5%.
The failure lives in the 95%.
That's not a reflection on your judgment.
That's a property of the failure mode.
The three checks
The gap almost always lives in one of three places. Check them in order.
Check 01
Artifact and environment parity
Are staging and production actually running the same bytes?
Not the same tag. The same bytes. Model checksum. Container image digest. Library versions. GPU precision settings. Whether model.eval() is being called on every inference code path. This sounds obvious. It almost never gets checked when a model starts failing. Partial rollouts, rebuilt container tags, and library version drift routinely leave production running a different preprocessor than staging — with the same model weights. The outputs diverge silently. Nobody connects the dots because the model didn't change.
This single check surfaces 10–20% of production ML failures in five minutes.
Check 02
Inference equivalence
Does your model produce the same outputs in both environments for identical inputs?
Take 50–100 production inputs. Run them through your model in staging and production. Compare the raw logits before any post-processing. Large divergence with identical artifacts means a runtime environment bug — TF32 precision on different GPU architectures, cuDNN algorithm differences, library version behavior changes. Logits match but predictions diverge means a post-processing bug. Everything matches — the problem is in the features.
Check 03
Log-and-replay feature diff
Are training and serving computing the same features from the same raw inputs?
This is the check nobody runs. And it's where the problem almost always lives. The code that prepares data for training and the code that prepares data for inference are almost never identical. They start identical. Then they drift. The model learns on one version of your data. It predicts on another.
DoorDash: 4.3 percentage-point AUC gap. One feature with a 15-day lookback in training and 30-day in serving. Silent for months. 4.3 points. One number.
Two paths
Path 01
Implement this myself
Three implementation guides with exact commands, working code, interpretation. Start with Check 01. Takes five minutes. If you find something, you've saved yourself weeks.
Free · No account · No email
Path 02
Moe does this for me
12-question intake, 30 minutes. I personally run all three checks against your system plus the interpretation layer from 40+ systems across nine verticals.
Within 48 hours: Three numbered failure modes. Root cause. Fix sequence. One finding solved on camera. Answers to the questions you actually need.
$1,500
Five spots this month · Full refund if fewer than 3 findings
Get diagnosed — $1,500 →48 hours · Moe personally · No calls · No commitment beyond $1,500
The Ground Truth Framework
The complete diagnostic playbook — 125 pages.
Full research foundation (Sculley, Zhang, Breck, DoorDash post-mortem). Every check with runnable code and interpretation tables. Four detailed case studies across defense, medical, industrial, and SaaS. Vertical-specific guides. Bibliography.
Who this is for
You built or are building a production AI system. It works in testing. It fails in deployment.
You've tried fixing it — contractors, retraining, architecture changes. None of it worked.
You're in one of these situations:
- Demo approaching. Defense, investor, client. The system needs to hit a specific threshold by a specific date.
- Regulatory deadline. FDA submission, field validation study. AI component failing outside the lab.
- Client at risk. Industrial or enterprise client losing patience. Contract on the line.
- Investor pressure. Broken AI feature that's central to your pitch. Series A or B conversation happening now.
- The private thought you haven't said out loud. Maybe the problem is me. Maybe I hired wrong. Maybe this isn't fixable.
It's fixable. And the problem is almost certainly not what anyone has been looking at.
What changes after the diagnosis
“The demo was in 10 days. I was already mentally drafting the email to my program officer. Moe found a TF32 precision issue on our A100 cluster and a preprocessing divergence introduced when we switched serving frameworks. One config flag and a pipeline fix. No retraining. We demoed at 91% accuracy.”
— Co-founder, defense tech startup · UAS classification · DoD program
“We had been through two contractors and eight months of failed retraining attempts. Nobody had ever compared what the serving pipeline computed to what the training pipeline computed on the same inputs. Three features had 40–80% disagreement rates. Eight months. Three features. Two days to fix.”
— CTO, industrial automation startup · Quality inspection · Automotive
“I almost didn't do this because I'd already spent so much on contractors. What made me do it was the guarantee. If Moe couldn't find three specific findings I'd get my money back. He found seven.”
— Head of ML, Series B SaaS · Recommendation systems
The Guarantee
Three specific findings or every dollar back.
If I don't identify at least three specific numbered deployment failure modes — each with root cause and fix sequence — you get every dollar back. 100%. No questions. No conditions.
The criteria are countable. Either there are three specific numbered findings or there aren't. You will know the moment you receive the report.
This guarantee has never been triggered.
The price in context
A senior ML contractor costs $1,200–$2,400 per day. The engagement you've already been through likely cost $30,000–$150,000. Cleveland Clinic charges $1,690 for a specialist second opinion that changes 67% of diagnoses.
That's the category this belongs in. Not consulting. Diagnosis.
Five spots this month
48 hours · Moe personally · Full refund if fewer than 3 findings · No calls