Guide your enterprise through complex AI transformations. We deliver MLOps maturity audits, GPU compute cost optimization, data pipeline reviews, and zero-trust AI governance roadmaps.
Objective technical evaluations engineered to optimize model performance, slash cloud GPU spending, and enforce data security.
Evaluate your end-to-end Machine Learning lifecycle. We audit model CI/CD pipelines, feature store latency, vLLM inference serving, and model monitoring telemetry.
Eliminate wasted GPU compute resources. We inspect NVIDIA H100/A100 cluster allocation, vLLM/TensorRT batch sizes, spot instance auto-scaling, and cloud storage tiers.
Design a modern, zero-bottleneck data foundation. We review Apache Kafka streaming rates, Snowflake/Databricks warehousing queries, and dbt transformation pipelines.
Protect proprietary data and satisfy strict regulatory bodies. We author zero-trust AI security frameworks, PII masking rules, and SOC 2 / EU AI Act compliance roadmaps.
Strategic technology consulting delivered by veteran AI systems architects and data engineering leads.
Designing multi-cloud AI infrastructure roadmaps tailored for distributed LLM inference, RAG search, and autonomous agent workloads.
Auditing NVIDIA H100/A100 cluster allocation and model quantization (vLLM/TensorRT) to reduce cloud AI compute bills by 40%+.
Eliminating pipeline bottlenecks, tuning Kafka streaming partitions, and restructuring Snowflake and BigQuery warehousing queries.
Assessing automated model deployment pipelines, feature store latency, and model monitoring telemetry against industry best practices.
Scanning AI models for PII leakage, prompt injection vulnerabilities, and authoring SOC 2, HIPAA, and EU AI Act compliance roadmaps.
Formulating risk-free Strangler Fig roadmaps to embed AI capabilities directly into legacy ERP, CRM, and mainframe systems.
We provide vendor-neutral architectural guidance across all leading enterprise AI and data platforms.
Wraxel conducted a 3-week AI architecture audit for a high-growth SaaS platform facing $4.2M/yr AWS GPU bills and 14-second AI inference latency. By re-architecting model serving on vLLM with spot instance auto-scaling, we slashed annual cloud GPU spend by 48% ($2M+ savings) while dropping inference latency to under 180ms.
Request Case Study BriefingA proven 4-stage technical advisory framework delivering clarity, cost optimization, and measurable AI ROI.
Deep-dive inspection of AI model codebases, GPU cluster utilization, data pipelines, and cloud bills.
Identifying performance bottlenecks, security risks, vendor lock-in, and hidden infrastructure cost leaks.
Authoring 90-day actionable execution plan, vLLM/quantization guidelines, and multi-cloud ROI projections.
Providing hands-on engineering guidance, code reviews, vendor negotiation support, and SOC 2 verification.
Everything you need to know about our Enterprise AI Architecture & Data Strategy Consulting.