AI Integration & Deployment
We connect AI capabilities into your existing products and workflows — reliably, securely, and at production scale.
What's included
- REST and gRPC API development for AI models
- Cloud deployment on AWS, GCP, and Azure
- Containerization with Docker and Kubernetes
- A/B testing and gradual rollout infrastructure
- Real-time and batch inference pipelines
- Security review and compliance alignment
Our approach
Integration Design
We map the AI capability to your existing architecture — identifying integration points, data flows, and the reliability requirements for each.
Infrastructure Setup
We provision and configure the serving infrastructure: containerized model servers, auto-scaling policies, load balancers, and monitoring hooks.
Staged Rollout
We deploy behind a feature flag, shadow-test in production traffic, then gradually route real users — catching issues before they affect your entire user base.
Monitoring & Handoff
We instrument your deployment with latency, error rate, and drift dashboards, then hand off to your team with runbooks and on-call playbooks.
What you get
- Production deployment infrastructure (IaC templates)
- Model serving API with authentication
- CI/CD pipeline for model updates
- Monitoring dashboards and alerting
- Load testing report
- Operations runbook
Technologies we use
- Docker
- Kubernetes
- AWS SageMaker
- Google Vertex AI
- Azure ML
- FastAPI
- Triton Inference Server
- Redis
- Kafka
Related services
Ready to get started?
Tell us about your project and we'll come back with a concrete plan within one business day.
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