ML Deployment Engineering
Your model works in testing.
We make it work everywhere else.
Production AI diagnosis and remediation for mission-critical systems. Defense, medical, industrial, and beyond — where a broken AI isn't just a bad metric. It's a failed demo, a lost contract, a delayed clearance, a system that doesn't perform when it matters most.
The gap contractors keep missing
In 2015, a team at Google published research showing that model code represents roughly 5% of a mature production ML system. The other 95% is infrastructure, pipelines, configuration, and the boundary between training and deployment.
Every contractor hired to fix a broken AI system reaches for the same toolkit — architecture reviews, training curves, accuracy metrics. All of it aimed at the 5%.
This isn't negligence. It's the methodological blind spot built into how ML engineering is taught and practiced. The industry pointed its flashlight at the model. The failures live everywhere else.
Training distribution mismatch
Your model memorized your lab. One controlled environment, one specific distribution of inputs, one set of conditions that existed at collection time. Production is a different world. Different users. Different conditions. Different inputs the model has never seen. The model doesn't fail because it was built wrong. It fails because it was never shown the world it needed to work in. More data doesn't fix this. A bigger architecture doesn't fix this. It fixes when you close the gap between the training distribution and the production distribution.
Environment divergence
Your contractors evaluated the model on their machines. Your users run it on yours.
Different GPU architectures produce different numerical outputs for identical inputs. NVIDIA's Ampere and Hopper GPUs default to TF32 precision — silently changing floating-point behavior. Different library versions change preprocessing behavior. A forgotten model.eval() call means batch normalization recomputes on every request.
Same model weights. Same code. Different world. Different outputs.
Preprocessing pipeline drift
Training and serving pipelines start identical. Then they drift. Someone fixes a bug in training and doesn't update serving. Someone adds a feature in Python and reimplements it in Go. A library updates and changes default behavior. Small differences compound silently. The model learns on one version of your data. It predicts on another. The gap is invisible in your offline metrics — because your evaluation uses the training pipeline, not the serving pipeline. It only appears in production. By then everyone has already blamed the model.
Invisible from inside
Here's why all three failure modes persist through multiple contractors and months of debugging. Everyone inside the project is standing on the same side of the gap. The same training environment. The same test conditions. The same data pipeline. They all see the same view. The gap between where the model learned and where it runs is only visible from outside. That's the only thing we do differently. We stand outside.
The Ground Truth diagnostic process
A systematic three-layer diagnostic that finds what contractors miss. Applied personally to every system we work on. Not a framework we hand off — a process we run.
Before touching anything else — confirm staging and production are running the same bytes. Model checksums. Container image digests — not tags, digests. Library version lockfiles. GPU architecture and driver versions. TF32 precision settings. This sounds like basic DevOps. It almost never gets checked when a model starts failing. This single check surfaces 10-20% of production ML failures. In five minutes.
Take 50-100 representative production inputs. Run them through the model in both environments. Compare the raw logits before any post-processing. Healthy systems show argmax agreement above 99.9% and maximum absolute logit difference below 0.0001. Large divergence with identical artifacts means the bug is in the runtime. Everything matches — the problem is in the features.
The check nobody runs. Where the problem almost always lives. Collect production prediction logs. Replay raw inputs through the training pipeline. Compare what the serving pipeline computed versus what training would have computed on identical raw inputs. Any divergence is a confirmed preprocessing skew bug. DoorDash published a post-mortem about exactly this. 4.3 percentage-point AUC gap. One feature. Silent for months.
Three numbered failure modes. Root cause for each. Fix sequence in the right order. The order matters as much as the findings. Fix the pipeline first. Get a clean signal. Then measure what's actually there. Then address it. One finding fixed on camera, same day, before you read the rest of the report.
Three ways to work with us
From free self-service to full-engagement remediation. Every path ends at the same place — a production AI system that works in the field, not just the lab.
Do It Yourself
The Ground Truth Framework
- →The complete diagnostic framework, published
- →Three checks with exact code to run each one
- →Artifact parity, inference equivalence, log-and-replay
- →PSI, KS-test, SHAP-weighted prioritization
Free
No account required
Done With You
Ground Truth Diagnosis
- →Moe personally applies the framework to your system
- →Three numbered failure modes with root cause + fix sequence
- →One finding fixed completely on camera, same day
- →Answers: fixable before demo? How long? Rebuild or fix?
- →5 spots per month — structural constraint
$1,500
Full refund if fewer than 3 findings · 48h
Done For You
Full Deployment Remediation
- →End-to-end for mission-critical systems
- →Diagnosis, implementation, pipeline architecture
- →Distribution monitoring and drift detection
- →Ongoing retainer available
From $10K/mo
Engagement-based · Inquiry required
What changes after a diagnosis
“I want to be specific about what this is: it's not consulting, it's not a course, it's not a framework document. It's someone who has seen your exact failure pattern before telling you exactly what it is and exactly how to fix it. I'd spent four months convinced we had an architectural problem. We had a timezone handling bug and a stale scaler. Four hours of work once we knew what we were looking at. The diagnosis paid for itself before I finished reading the report.”
— Founder, Series A SaaS · Fraud detection · Fintech
“The demo was in 10 days. I was already mentally drafting the email to my program officer explaining why we weren't performing to spec. Moe found a TF32 precision issue on our A100 cluster and a preprocessing divergence introduced when we switched serving frameworks three months earlier. 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. When Moe ran that comparison the answer was immediately obvious — three features had 40-80% disagreement rates. Eight months. Three features. Two days to fix.”
— CTO, industrial automation startup · Quality inspection · Automotive
“I'll be honest — I almost didn't do this because I'd already spent so much on contractors and was skeptical anything would be different. 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. The report was 14 pages. The first fix alone — a rolling window mismatch — took our online AUC from 0.61 to 0.79.”
— Head of ML, Series B SaaS · Recommendation systems
“Six weeks from our FDA submission. The AI-assisted diagnostic component was failing on 22% of field samples. Lab accuracy was 96%. Moe found the distribution mismatch in 48 hours. We collected 800 samples from the field device, fine-tuned for two weeks, reached 91% on field samples. Submission went through.”
— CTO, medical AI company · Diagnostic imaging · FDA De Novo
Where we work
Nine verticals. All mission-critical. The common thread: a broken AI system has consequences that go beyond a bad metric.
Defense & UAS
DoD demonstrations. SBIR/STTR milestones. Autonomous classification, navigation, threat assessment. Where a failed demo costs a Series A and a DoD contract.
Medical Devices
FDA 510(k) and De Novo submissions. Field validation. Portable device deployment. Where a failed validation delays clearance by 6+ months at $50K–$200K/month.
Industrial Automation
Production line inspection. Predictive maintenance. Automotive, oil & gas, semiconductor. Where downtime costs $500K–$2.3M per hour.
Robotics & Autonomous
Navigation, localization, object detection. Lab-to-field performance gaps in systems where field conditions never match training conditions.
Fintech & Risk
Fraud detection, credit scoring, real-time trading. Where model failure isn't accuracy drop — it's regulatory exposure. CFPB ordered $89.8M for AI credit failures in 2024.
Enterprise SaaS
AI features that demo well and fail in production. Churn prediction, recommendation, NLP, search. Central to your pitch, your differentiation, your renewals.
The Ground Truth Guarantee
Three specific findings or every dollar back. Automatically.
If we 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 with root cause and fix sequence, or there aren't. You will know the moment you receive the report.
This guarantee has never been triggered.
Tell us about your system
Every inquiry gets a personal response within 24 hours. If you're in an urgent situation — demo in less than two weeks, FDA submission at risk — say so. We prioritize.
📘 Every inquiry also unlocks the 125-page Ground Truth playbook.
Response time
Within 24 hours
Availability
5 diagnosis spots per month
Free resource
The Ground Truth Framework