Glotech.io
AI Architecture
Glotech builds RAG pipelines with pgvector and Pinecone, designs agent orchestration with LangGraph, and implements guardrails that keep retrieval grounded. Output is auditable and traceable to source documents.
We build AI infrastructure
that runs while you sleep
Technologies we use
Kubeflow
Google
Cloud
Terraform
Prometheus
Grafana
Docker
Python
Kubernetes
AWSCore Capabilities
MLOps Engineering
Glotech deploys model training and serving pipelines on Kubeflow, provisions infrastructure with Terraform, and manages experiment tracking with MLflow. Teams get reproducible environments and faster iteration from commit to serving.
Platform Operations
Glotech runs Kubernetes clusters on EKS and GKE, automates node scaling and cost allocation with Terraform, and monitors uptime with Prometheus and Grafana. Infrastructure stays observable and under budget.
What We Do
š High Availability Systems
Glotech configures multi-AZ deployments, automated failover with Route 53 health checks, and self-healing Kubernetes pods. Clients maintain sub-minute recovery times during outages.
š Auto-Scalable Infrastructure
Glotech provisions Kubernetes HPA and Karpenter-based node autoscaling on AWS and GCP. Clusters scale with actual load and right-size automatically to control cloud spend.
āļø Cloud-Native AWS Architectures
Glotech designs AWS stacks combining SageMaker for model training, Lambda for event-driven logic, S3 for data lakes, and CloudWatch for alerting. Architected for workloads with strict compliance and audit requirements.
š DevOps & Observability
Glotech sets up CI/CD with GitHub Actions and ArgoCD, monitors services with Prometheus and Grafana, and configures PagerDuty alerting. Teams ship daily with full visibility into what's running.
š§ AI System Integration
Glotech connects ML models to production applications through typed API contracts, structured logging, and circuit breakers. Models serve predictions behind clear failure boundaries with fallback paths.
š Security & Compliance Engineering
Glotech implements IAM policies, VPC isolation, encryption at rest with KMS, and audit logging for SOC 2 and GDPR compliance. Security controls are automated into the deployment pipeline.
Computer Vision
Production-ready computer vision systems, from data to deployment. Built for reliability, iteration, and real-world constraints.
Production-Ready Vision Systems
Glotech builds inference pipelines with ONNX Runtime and TensorRT, served behind FastAPI endpoints with health checks and structured logging. Every prediction is traceable.
Data-Centric Development
Glotech manages datasets with DVC for versioning and Label Studio for annotation workflows. Model accuracy improves through systematic data review, not just bigger models.
Train, Deploy, Iterate
Glotech trains YOLOv8 and custom detection models with reproducible MLflow experiments, deploys via canary rollouts, and retrains on production feedback loops.
Edge & Cloud Deployment
Glotech deploys vision models on edge devices (Jetson, Coral) for low-latency inference and on GPU-backed Kubernetes for cloud workloads. Latency, throughput, and cost are profiled before going live.
Dataset Management & Versioning
Glotech versions datasets with Git LFS and S3-backed storage, enabling side-by-side comparison of training runs. When a model regresses, teams trace the issue to the exact data change that caused it.
Monitoring, Drift & Human-in-the-Loop
Glotech monitors prediction confidence with Prometheus, flags distribution drift with Evidently, and routes low-confidence samples to human reviewers. Models stay accurate as real-world data shifts.
Kaya
Kaya converts existing factory CCTV into CNC machine runtime reports. Computer vision reads tower light states. No new hardware, fully automated on AWS.
Tower light analysis
AI models classify each machine's tower light as green, yellow, red, or off. Machine uptime, idle time, and faults calculated automatically from the camera feed.
No new hardware
Uses cameras already mounted on the factory floor. No sensors, no PLCs, no edge boxes. Works with Fanuc, Siemens, Heidenhain, and Mitsubishi CNC machines.
Fully automated pipeline
Three SageMaker pipelines handle auto-annotation, model training, and activity reporting. Human review gate on every label. Models improve from real production data.