Enterprise Machine Learning - Architecture

Bridge the gap between experimental Jupyter research notebooks and resilient, low-latency production ML inference. Aurevon trains custom neural models, fine-tunes domain-specific LLMs, and builds automated MLOps pipelines on AWS SageMaker on fixed USD milestone contracts.

Production Architecture

Venture-backed Series A-C startups, algorithmic fintech platforms, healthcare diagnostic scale-ups, and enterprise SaaS companies building proprietary AI moats.

Who Is It For

Venture-backed Series A-C startups, algorithmic fintech platforms, healthcare diagnostic scale-ups, and enterprise SaaS companies building proprietary AI moats.

Typical US Use Case

A New York fintech scale-up needed a custom fraud detection model to evaluate 8,000 transactional webhooks per second. We trained and deployed an ensemble LightGBM/PyTorch model on AWS SageMaker with sub-18ms inference latency, catching 94.2% of fraudulent attempts.

Enterprise Security

Engineered for high-concurrency US traffic surges, low-latency nationwide delivery, and resilient cloud uptime.

Compliance & Tech Considerations

Engineered in PyTorch, TensorRT, and Triton Inference Server on AWS us-east-1. Automated model artifact versioning with MLflow and DVC. SOC 2 Type II compliant model explainability (SHAP) and data anonymization pipelines.

Production Scale & Quantified Outcomes

Defensive data security protocols coupled with verified business results.

Verified Production Outcomes

Reduced model inference latency from 140ms to 18ms, automated weekly model retraining with zero data drift, and improved fraud detection accuracy by 26%.

Verified Engineering Standards

Every milestone is governed by a fixed USD Statement of Work, unencumbered intellectual property assignment under US law, automated CI/CD suites, and weekly Friday staging deployments.

AWS US Multi-Region (us-east-1 & us-west-2) & SOC 2 Type II

Every platform is deployed with verified domestic US cloud hosting, automated CI/CD coverage, and strict compliance with SOC 2, HIPAA, and CCPA standards.

100% US Commercial IP Assignment & Fixed USD Milestone Quotes

Full Git repository ownership and commercial copyright transferred under US law with milestone disbursements in USD ($) via ACH or wire and zero retainers.

Frequently Asked Questions (United States)

Direct answers to questions US founders and engineering directors ask us before getting started.

How do you transition machine learning models from research notebooks to production APIs?+

We containerize models using Docker and Triton Inference Server, optimize weights via TensorRT/ONNX, and deploy auto-scaling inference endpoints on AWS SageMaker or ECS with sub-30ms SLAs.

How do you prevent model performance degradation and data drift over time?+

We build automated telemetry pipelines that monitor incoming feature distributions against training baselines, alerting engineers to drift and triggering automated retraining pipelines.

How do your machine learning pipelines comply with SOC 2 Type II and HIPAA data rules?+

Training datasets are cryptographically tokenized and hosted in encrypted S3 enclaves using KMS customer-managed keys with role-based access control and automated CloudTrail audit logging.

How do your ML systems architects collaborate with US engineering teams on working hours?+

Our engineers operate in full synchronous alignment with your business hours across ET, CT, MT, and PT, providing direct Slack collaboration, live model evaluation walkthroughs, and weekly commits.

Who owns the trained model weights, preprocessing code, and training pipelines?+

Your company retains 100% full unencumbered ownership of all custom model weights, training scripts, data preprocessing code, and architecture runbooks under our US commercial MSA.

What payment options and tax documentation do you provide for US enterprises?+

We provide standard W-9 documentation, execute clear fixed USD milestone Statements of Work, and support domestic ACH bank transfers, Fedwire, and Net-30 purchase orders.

Initiate US Technical Briefing →