BGMTech
Intelligent Infrastructure
AI & Cloud
Intelligent infrastructure, real results
01Data Pipeline
02Model Training
03Cloud Dashboard
04Deployment
05Monitoring
06Optimization
AI & Cloud Infrastructure in 2026
Intelligentsystemsthatlearn
andscalewithoutyou.
We build with Python — the same infrastructure running recommendation engines, fraud detection, and real-time analytics at scale. You get that level of engineering without the enterprise overhead.
Why Custom AI
ChatGPT is a product. Your AI should be an asset.
Here's what happens when you build AI on someone else's foundation.
“Just use the ChatGPT API”
HoverTap to flipUntil your prompts leak proprietary data, OpenAI changes pricing overnight, and your product breaks because someone else’s model update shifted behavior. You built on rented intelligence.
“The cloud is too expensive”
HoverTap to flipUntil you realize on-prem servers cost more in maintenance, electricity, and ops salaries than a right-sized cloud deployment. We’ve cut client cloud bills by 40%. The cloud isn’t expensive — misconfiguration is.
“AI will replace my team”
HoverTap to flipUntil the model hallucinates customer data and nobody on staff knows how to fix it. AI augments human judgment — it doesn’t replace domain expertise.
“We’ll train our own model”
HoverTap to flipUntil you’re 6 months and $200K deep into a project that a fine-tuned open-source model could have handled in weeks. Not every problem needs training from scratch.
“Kubernetes is overkill”
HoverTap to flipUntil your single-server deployment goes down at peak traffic with no failover, no rolling updates, and no way to scale. K8s isn’t overkill — under-engineering is.
“We’ll figure out DevOps later”
HoverTap to flipUntil your first production deploy takes 4 hours of manual steps, breaks something, and nobody can roll back. CI/CD isn’t a luxury — it’s how you ship with confidence.
We're not against SaaS AI or managed services — they have their place. But when you need custom models, controlled infrastructure, and predictable costs, that's where we come in.
Our Approach
How we architect
Cloud-native pragmatism
Right-sized, not over-engineered.
AWS runs Netflix. GCP runs Spotify. Azure runs LinkedIn. These aren't exotic platforms — they're where the world's data lives. We don't pick a cloud provider because it's trendy. We pick the one that matches your compliance requirements, latency needs, and budget. Multi-cloud when it makes sense. Single-provider when it doesn't.
AI where it moves the needle.
We build AI that solves specific, measurable problems — a chatbot that reduces support tickets by 35%, a recommendation engine that increases average order value, a document processor that eliminates 10 hours of manual data entry per week. If a rule-based system solves the problem, we'll tell you. We don't bolt on AI for the pitch deck.
Purpose-built intelligence
Self-healing deployments
Infrastructure that heals itself.
Every deployment pipeline we build includes automated rollbacks, health checks, and zero-downtime strategies. Container orchestration with Kubernetes means your services scale under load and recover from failures without paging your team at 3 AM. Infrastructure-as-code with Terraform means your entire environment is reproducible, auditable, and version-controlled.
The Stack
What your infrastructure runs on
AIAI & Machine Learning
Python
Anthropic
OpenAI
TensorFlow
PyTorch
FastAPI
PostgreSQL
Redis
N8N
Elasticsearch
GraphQL
Prisma
Python
Anthropic
OpenAI
TensorFlow
PyTorch
FastAPI
PostgreSQL
Redis
N8N
Elasticsearch
GraphQL
Prisma
Python
Anthropic
OpenAI
TensorFlow
PyTorch
FastAPI
PostgreSQL
Redis
N8N
Elasticsearch
GraphQL
Prisma
>>>Cloud Providers
AWS
Azure
GCP
Vercel
Railway
Cloudflare
Supabase
Firebase
InfluxDB
DynamoDB
AWS
Azure
GCP
Vercel
Railway
Cloudflare
Supabase
Firebase
InfluxDB
DynamoDB
AWS
Azure
GCP
Vercel
Railway
Cloudflare
Supabase
Firebase
InfluxDB
DynamoDB
$DevOps & Infrastructure
Docker
Kubernetes
Linux
Nginx
Terraform
Ansible
Git
GitHub
GitLab
Jenkins
Grafana
Prometheus
Kafka
RabbitMQ
Vault
Docker
Kubernetes
Linux
Nginx
Terraform
Ansible
Git
GitHub
GitLab
Jenkins
Grafana
Prometheus
Kafka
RabbitMQ
Vault
Docker
Kubernetes
Linux
Nginx
Terraform
Ansible
Git
GitHub
GitLab
Jenkins
Grafana
Prometheus
Kafka
RabbitMQ
Vault
The Process
From problem to production AI
01
Discovery
- Problem definition
- Data audit
- Feasibility assessment
02
Model Dev
- Data pipeline setup
- Training/fine-tuning
- Evaluation
03
Cloud Arch
- Infrastructure-as-code
- Container orchestration
- CI/CD pipeline
04
Deploy
- Zero-downtime launch
- Security scanning
- Cost guardrails
05
Monitor
- Drift detection
- Performance dashboards
- Continuous retraining
01
Discovery
- Problem definition
- Data audit
- Feasibility assessment
02
Model Dev
- Data pipeline setup
- Training/fine-tuning
- Evaluation
03
Cloud Arch
- Infrastructure-as-code
- Container orchestration
- CI/CD pipeline
04
Deploy
- Zero-downtime launch
- Security scanning
- Cost guardrails
05
Monitor
- Drift detection
- Performance dashboards
- Continuous retraining
Your Visibility
You see everything. In real time.
No black boxes. No “the model just works.” You understand every decision, every metric, every cost.
01
Cost Dashboard
Every cloud resource has a tag, a budget alert, and a purpose. You see real-time spend broken down by service, environment, and team. No surprise invoices — ever.
02
Model Performance Metrics
Accuracy, latency, drift scores — all visible in a shared Grafana dashboard. When model performance degrades, you know before your users do.
03
Deployment Pipeline Visibility
Every commit triggers a pipeline you can watch. Build, test, scan, deploy. Green or red — the status is public. No manual deploys, no “it works on my machine.”
04
Runbooks & Documentation
Every deployment has a runbook. Every model has a data card. Every infrastructure choice is documented with the reasoning behind it. Your next team can operate everything independently.
Architecture
How a production AI system comes together
Ingestion
REST APIWebhooksMessage QueueFile Upload
Processing
Model InferenceData PipelineFeature StoreValidation
Orchestration
KubernetesDockerTerraformGitHub Actions
Data
PostgreSQLRedisS3/BlobVector DB
Observability
GrafanaPrometheusAlertManagerCost Explorer
99.9% Uptime3 Models Active8 Services2 Regions
AI Layer
Your models don't demo. They deploy.
Every AI model we ship comes with monitoring, retraining pipelines, and fallback strategies. Latency budgets are set before training begins. If inference takes too long, the system degrades gracefully — not catastrophically.
Models are versioned alongside code. Rollbacks take seconds. A/B testing between model versions happens at the infrastructure level, not in your application logic.
Production AI isn't a Jupyter notebook. It's a system — and we engineer it like one.
inference — ai-gateway
3 models activeJetPal NLPactive
Chat — Intent ClassificationDocument Parseractive
OCR — Data ExtractionPricing Engineactive
Regression — Rate PredictionLast inference
POST /v1/inference/jetpal
→ intent: fleet_availability (0.94)
→ response_time: 118ms
→ intent: fleet_availability (0.94)
→ response_time: 118ms
healthy · 2.4K req/dayavg latency: 168ms
models: healthyGPU: 34%
gateway v3.1pipeline — deploy/production
$ git push origin main
Build............. ✓ 23s
Unit Tests........ ✓ 47s (312 passed)
Security Scan..... ✓ 12s (0 vulnerabilities)
Container Build... ✓ 34s (image: 142MB)
Staging Deploy.... ✓ 8s
E2E Tests......... ✓ 1m 22s (28 scenarios)
Prod Deploy....... ✓ 0s (rolling update)
✓ v2.4.1 live — 0 downtime — 3m 26s total
CI/CD PipelineFrom commit to production.
From commit to production.
In minutes, not meetings.
Every push to main triggers a fully automated pipeline: build, test, security scan, container build, staging deploy, E2E tests, and production deploy. No manual steps. No deployment Fridays.
Rolling updates mean zero downtime. Canary deployments mean new versions serve 5% of traffic before going full. Automated rollbacks mean a bad deploy is undone in seconds, not hours.
Your infrastructure ships as reliably as your code. Because it is code.
By The Numbers
0+AI Models Deployed
0.9%Infrastructure Uptime
0%+Avg Cost Reduction
0+CI/CD Pipelines
0+Cloud Services Managed
Our Work
Projects powered by this infrastructure
AI Chatbot
JetPal Chatbot
Intelligent conversational assistant integrated into the Jets & Partners website for instant client support.
PythonNext.js 14OpenAI APIWebSocketPostgreSQLRedis+2
ERP/CRM System
SpiderJet ERP
Enterprise-grade microservices platform with 31 services covering flight ops, billing, CRM, and monitoring.
Go/BeegoPythonNode.jsNext.js 14SvelteKitPostgreSQL+5
Aviation Booking API
Booking Engine
Commercial booking API with client portal, admin dashboard, rate limiting, and multi-tier pricing.
Go/BeegoSvelteKitAstroPostgreSQLMongoDBRedis+2
Let'stalkaboutyourAIandcloudstrategy.
Tell us what problem you're solving. We'll tell you whether AI is the right tool — and if it is, how we'd build it.