Glotech
AI Architecture
Retrieval-augmented generation (RAG) pipelines, agent workflows, and safety boundaries designed for measurable outcomes. We ensure your agents don't hallucinate.
Enterprise AI & MLOps
Infrastructure Engineering
Kubernetes automation, Terraform IaC, and production MLOps pipelines.
Built for scale, reliability, and enterprise cloud platforms.
Technologies we use
Kubeflow
Google
Cloud
Terraform
Prometheus
Grafana
Docker
Python
Kubernetes
AWSInfrastructure that never sleeps
We design practical systems that perform under real business constraints. No black boxes, just reliable engineering.
Core Capabilities
MLOps Engineering
Glotech implements MLOps pipelines using Kubeflow and Terraform to reduce model deployment latency and ensure environment reproducibility across enterprise cloud stacks.
Platform Operations
Automating Kubernetes (EKS/GKE) scaling with Terraform to optimize cloud spend and maintain 99.9% infrastructure availability for mission-critical AI workloads.
What We Do
🚀 High Availability Systems
Design and operate mission-critical systems with zero downtime and robust failover strategies to keep your business running 24/7.
📈 Auto-Scalable Infrastructure
Build cloud-native platforms that scale seamlessly with demand, optimizing both cost and performance for your growing needs.
☁️ Cloud-Native AWS Architectures
Design secure, auto-scalable systems using AWS services like SageMaker, Lambda, S3, and CloudWatch — ideal for finance, AI, and compliance-heavy workloads.
🔍 DevOps & Observability
Implement CI/CD pipelines, monitoring, and alerting for total system visibility and rapid iteration cycles.
🧠 AI System Integration
Integrate machine learning and AI components into existing products and workflows with clear contracts, observability, and failure boundaries.
🔒 Security & Compliance Engineering
Design systems that meet enterprise security, privacy, and compliance requirements without slowing down delivery or operations.
Computer Vision
Production-ready computer vision systems — from data to deployment. Built for reliability, iteration, and real-world constraints.
Production-Ready Vision Systems
End-to-end computer vision pipelines designed to run in production. Not demos — reliable, auditable systems.
Data-Centric Development
Dataset versioning, labeling, and review workflows that improve model performance through better data.
Train, Deploy, Iterate
Object detection, tracking, and classification with reproducible training, safe deployment, and continuous improvement.
Edge & Cloud Deployment
Vision models deployed on edge, cloud, or hybrid architectures — optimized for latency, scale, and cost.
Dataset Management & Versioning
Control datasets over time with versioning, comparison, and traceability to ensure reproducible training and reliable model behavior.
Monitoring, Drift & Human-in-the-Loop
Track model performance in production, detect data and concept drift, and route edge cases to human review to keep vision systems accurate over time.