AWS Cloud Infrastructure + CI/CD Automation for a Media Intelligence Platform
We designed and implemented a secure, scalable AWS cloud foundation for an AI-driven media intelligence company that helps advertisers and agencies make decisions using verified ad spend data.
Industry: AdTech / Media Intelligence / Marketing Analytics
Company size: ~150 employees (public estimates vary)
Location: New York, NY, United State
Project duration: 3 years
Tech focus: AWS, Terraform (IaC), Jenkins + GitLab CI/CD, Docker, Kubernetes/ECS, CloudWatch, New Relic, multi-account / multi-region networking
About the project
Client Overview
Client is creating its vision of the future for media buying and selling: a trusted intelligence platform for buyers, sellers, and platforms, with products like an AI Agent that turns plain-English questions into real-time charts and actionable insights sourced from verified data.
Challenges
- Establish reproducible infrastructure and eliminate environment drift
- Reduce release friction via end-to-end CI/CD automation
- Standardize containerization and orchestration for reliability and scaling
- Ensure secure AWS service integrations across environments
- Implement monitoring + alerting to improve uptime and incident response
- Build multi-account / multi-region networking, including Transit Gateway peering and secure cross-account access
Our Approach
We treated infrastructure as a product:
Codify everything (Terraform) → 2) Automate delivery (Jenkins + GitLab) → 3) Standardize runtime (Docker + Kubernetes/ECS) → 4) Strengthen reliability (autoscaling + observability) → 5) Secure connectivity (Transit Gateway peering, cross-account communication).
Work Phases
Phase 1 - Architecture & Security Baseline
- Target AWS architecture, environment separation, security requirements
- Multi-account / multi-region connectivity design
Phase 2 - Infrastructure as Code (Terraform)
- Terraform modules and reproducible provisioning
- Standardized patterns for repeatable environments
Phase 3 - CI/CD Automation (Jenkins + GitLab)
- Automated build/test/deploy pipelines
- Added caching mechanisms to speed up delivery
Phase 4 - Containers & Orchestration (Docker + Kubernetes/ECS)
- Containerized applications with Docker
- Orchestration via Kubernetes/ECS aligned with reliability and scaling needs
Phase 5 - AWS Integrations
- Secure integration with S3, CloudFront, SES, RDS, Lambda, Elastic Beanstalk
- Cross-account access and secure environment-to-environment communication
Phase 6 - Monitoring & Alerting (CloudWatch + New Relic)
- CloudWatch-based monitoring and alerts
- New Relic observability for deeper insights and faster incident response
Phase 7 - Networking (Transit Gateway Peering)
- Transit Gateway peering across multiple accounts and regions
- Controlled, secure connectivity between environments
Tech Stack
- AWS: S3, CloudFront, SES, RDS, Lambda, Elastic Beanstalk, CloudWatch, Transit Gateway
- IaC: Terraform
- CI/CD: Jenkins, GitLab
- Containers: Docker
- Orchestration: Kubernetes, ECS
- Observability: CloudWatch, New Relic
Key Results
70% faster deployments through CI/CD automation
Consistent, reproducible infrastructure with Terraform
Higher uptime and reliability via autoscaling + monitoring
Improved developer productivity through CI/CD + caching
Secure cross-account communication enabled between environments
Numbers
Deployment time: –70%
Uptime: 99.995%
Incident Reduction: –146.5%
Release Frequency: the same, but the time reduced from hours to minutes
MTTR <1 day
Want deployments to be 2-3× faster?
We’ll automate build/test/deploy and remove the manual steps slowing your team down.
Business Impact
By standardizing infrastructure delivery and operational visibility, the team moved from “manual and inconsistent” to a repeatable, scalable system where releases are faster and environments are stable. The result is fewer operational surprises and more time spent shipping product value instead of firefighting.
Team
Team Composition: 3 Dedicated DevOps Engineers.
Delivery Framework: Scaled Agile Framework (SAFe) / Scrum.
Operating Model: Cross-functional collaboration within Agile Release Trains (ARTs).
Timeline
Q3-Q4 2022 | Phase 1: Architecture, Security Baseline & Core Terraform
Q1-Q2 2023 | Phase 2: IaC Standardization & Legacy Migration
Q3-Q4 2023 | Phase 3: CI/CD Automation (Jenkins + GitLab) & Pipeline Optimization
Q1-Q2 2024 | Phase 4: Containerization & Orchestration (Docker + Kubernetes/ECS)
Q3-Q4 2024 | Phase 5: AWS Service Integrations & Secure Cross-Account Access
Q1-Q2 2025 | Phase 6: Advanced Networking & Transit Gateway Peering
Q3-Q4 2025 | Phase 7: Full-Stack Monitoring & Observability (CloudWatch + New Relic)
Q1 2026 | Phase 8: Optimization, Final Audits & Project Handover
Reviews
Our DevOps Team
Our engineers are not just experts in DevOps. We bring AI SDLC expertise across every stage, with human-on-the-loop development that keeps engineers in control. AI isn’t a bolt-on feature: the agents handle task analysis, estimation, code review, and commit discipline, making us 3.5x faster than engineers without AI. Therefore, our harness engineering integrates tooling and services to detect anomalies and prevent issues before they become critical incidents.
Is your infrastructure growing faster than your processes?
We’ll build a DevOps foundation that scales with your product - not against it.
Rate Apprecode as Your Partner
8 ratings, average 4.9 out of 5
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