RAG • Agents • AWS • MLOps
We build production-ready AI applications and cloud-native platforms with the engineering disciplines that matter after the prototype: reliability, security, observability, scalability, and cost efficiency.
AWS Certified Machine Learning • Enterprise Platform EngineeringSecuredPress is an AI engineering consultancy led by JP, Principal Consultant, bringing more than a decade of infrastructure, cloud, and software engineering experience across highly regulated and high-visibility environments, including banking, financial services, and large U.S. retail organizations. We build AI systems that can be operated, secured, measured, and improved in production.
Designed around real production constraints rather than demo-only success.
Cloud, security, and FinOps are engineering practices built into the solution.
AWS Certified Machine Learning – Specialty
AI Engineering · RAG · Agents · AWS · MLOps
Getting an LLM response is the easy part. Production systems need reliable retrieval, grounded answers, secure access, predictable latency, observability, and an architecture that remains affordable as usage grows.
Hybrid search, reranking, metadata controls, citations, and evaluation rather than vector similarity alone.
Measure quality, detect unsupported answers, add abstention guardrails, and make AI behavior observable.
Authentication, role-aware retrieval, IAM, private networking, encryption, secret management, and auditability.
Track tokens, latency, model usage, infrastructure utilization, and cost per workflow.
Focused engineering engagements for teams building RAG, agentic AI, document intelligence, and cloud-native LLM applications.
Enterprise search and grounded AI over internal knowledge.
Full-stack LLM applications, APIs, and agentic workflows.
Production deployment and operations across modern AWS environments.
Have an AI prototype that needs to become production software—or an existing system that needs better quality, reliability, or infrastructure?
Discuss Your AI ProjectA sanitized portfolio recreation based on an enterprise RAG solution developed for a large U.S. retail organization. The public demo uses a fictional gaming and hospitality company and entirely synthetic data.
Employees query operational procedures, IT documentation, HR policies, security standards, and internal knowledge through a grounded RAG experience with source citations and production controls.
Privacy note: no original client identity, documents, credentials, or proprietary data are included.
FinOps remains part of production AI engineering. Estimate potential optimization opportunities across SageMaker and Bedrock workloads.
Pricing: AWS on-demand, us-east-1
Estimates based on AWS on-demand pricing (us-east-1) and typical audit findings. Actual savings vary by workload, utilization patterns, and reserved capacity. Book a call for a scoped estimate specific to your environment.
Clarify the business workflow, data sources, users, quality requirements, security constraints, and production environment before choosing models or frameworks.
Implement retrieval, prompting, agents, structured outputs, APIs, or document processing with measurable behavior and clear interfaces.
Add authentication, security controls, persistence, testing, observability, deployment automation, scalability, and failure handling.
Measure answer quality, retrieval performance, latency, reliability, token usage, and infrastructure cost—then iterate based on evidence.
Tell us what you are building, what is already working, and where you need help—from RAG and agents to AWS deployment, security, reliability, or cost.