About Opulion
Built by someone who has been inside the systems that break.
Not a consulting firm. Not an agency. One engineer with 10+ years across infrastructure, software, and production AI — and a framework built from 40+ systems that failed in the field.
Why I built this
Three years ago I looked at a production ML system as an external consultant for the first time. The team had spent four months trying to fix it. Two contractors before me. Both credentialed. Both confident. Both looked at the same thing — the model — and both left the system broken.
I found the root cause in 30 minutes. Not because I was smarter. Because I was standing outside the system looking at the boundary between training and deployment. Something nobody inside the project had thought to check.
The gap between where the model learned and where it runs.
Since then I've done this 40+ times. Defense startups. Medical device companies. Industrial automation. Robotics. SaaS. Fintech. Same root cause. Every time. The gap between where the model learned and where it runs.
I built Opulion because the field needed someone who specializes in that gap. That's what we do.
10+ years across the stack
I am not a pure ML researcher. I am not a DevOps engineer. I am not a software architect. I am an ML deployment engineer — which means I have enough depth in all three to understand where they interact, and where the interactions break.
Pure ML engineers miss it because they don't think about the deployment boundary. Pure MLOps engineers miss it because they're focused on infrastructure reliability. Pure data engineers miss it because they're not looking at model behavior.
I've built things in all three domains. That's why I can see the gap.
Cross-domain experience
- →ML engineering — Model architecture, training pipelines, evaluation frameworks, feature engineering, drift detection. Computer vision, NLP, tabular, time-series, audio.
- →Infrastructure and deployment — Containerization, GPU cluster management, model serving, CI/CD for ML, cloud (AWS, GCP, Azure). Edge deployment for defense/industrial.
- →Industrial automation — PLC systems, SCADA integration, sensor data pipelines, real-time inference, HMI interfaces.
- →Defense-adjacent AI — Defense primes and startups, SBIR engagements, DoD milestone deliverables, UAS classification and autonomy systems.
- →Software architecture — Distributed systems, API design, data pipeline architecture.
What's different about how we look at broken systems
Most ML consultants start with the model. We start at the boundary.
The first thing we check is not whether the model is good. It's whether the model in production is the same model that was evaluated. Same bytes. Same weights. Same preprocessor. Same library versions. Same GPU precision settings.
Then we check whether the model sees the same inputs it was trained on. Then we check whether the production distribution matches training.
In 40+ systems, the failure has lived at one of those three boundaries every single time. Never in the model architecture. Always in the gap.
That's the entire framework.
What we're building beyond diagnostics
Ground Truth — the $1,500 diagnosis — is the entry point. But the larger work is remediation at the systems level.
Active projects include:
- →Edge ML deployment — Defense UAS platforms requiring real-time inference at the edge with strict latency and reliability constraints.
- →Adaptive PID control — Industrial hydraulic equipment integrating ML-driven adaptive control with traditional PLC automation.
- →AI deployment architecture — Medical device companies navigating FDA regulatory pathways for production AI systems.
We take five diagnosis clients per month. Full remediation engagements are fewer — three to four at any given time.
For press and referrals
Mostafa (Moe) is the founder of Opulion , an ML deployment engineering firm specializing in production AI diagnosis and remediation for mission-critical systems. He works across defense, medical device, industrial automation, robotics, and SaaS verticals.
His diagnostic framework — the Ground Truth Framework — identifies the gap between training and deployment environments that causes most production ML failures. In 40+ systems across nine verticals, the framework has identified at least three specific, fixable failure modes in every system examined.