Case Studies

The Challenge

A financial SaaS startup needed a production-grade AWS environment that could pass a security audit and support future compliance frameworks. Their existing setup was a single-AZ, manually provisioned mess with overly permissive IAM roles, no encryption on databases, and no audit trail.

The Solution

We designed a multi-VPC, multi-AZ architecture from scratch using Terraform, with strict network segmentation, encrypted storage everywhere, and full observability. All infrastructure is version-controlled, peer-reviewed, and reproducible from a cold start.

• Three-tier VPC with public, private, and isolated subnets across two AZs
• Terraform modules for every resource — no manual console changes
• RDS with encryption at rest, automated backups, and Multi-AZ failover
• S3 buckets with versioning, SSE-S3, and strict bucket policies
• IAM roles following least-privilege principle; no long-lived access keys
• CloudTrail, AWS Config, and GuardDuty for full audit and threat detection
• AWS WAF in front of the application load balancer

The Challenge

A legal firm needed to use AI to search and summarize internal case documents, but had a strict policy: no data could leave their private network. Every major LLM API was off the table. They needed a fully offline, privacy-first solution that non-technical staff could actually use.

The Solution

We deployed a fully self-contained LLM stack on an existing Ubuntu server using Docker Compose. The solution uses Ollama to serve a quantized LLaMA 3 model locally, FAISS for vector similarity search, and a lightweight Python API that connects user queries to the document corpus through a RAG pipeline.

• Ollama serving LLaMA 3 8B (Q4 quantized) entirely on-premises
• Document ingestion pipeline: PDF/DOCX → chunked → embedded → stored in FAISS
• Custom RAG API: query → retrieve top-k relevant chunks → LLM synthesis
• Docker Compose stack for reproducible deployment and easy updates
• Simple web UI for non-technical staff to submit queries
• Zero external API calls — no data egress of any kind

The Challenge

A marketing agency was sending campaigns through a shared SMTP server with a 60% deliverability rate — nearly half of all emails were landing in spam or bouncing. Campaign performance was tanking, and their ESP was throttling them. They needed a scalable, owned solution they controlled end-to-end.

The Solution

We replaced the shared SMTP setup with a self-hosted Mautic instance on AWS EC2, routed through Amazon SES for reliable sending. Proper email authentication (DKIM, SPF, DMARC) was configured through Route 53, and the entire stack was containerized for easy maintenance and scaling.

• Dockerized Mautic on EC2 with persistent volumes and automated backups
• Amazon SES configured with dedicated sending domain and IP warming
• DKIM, SPF, and DMARC records set up via Route 53
• Bounce and complaint handling routed through SES SNS notifications
• MySQL on RDS for Mautic database with automated snapshots
• Nginx reverse proxy with SSL termination via Let's Encrypt
• CloudWatch alarms for send rate, bounce rate, and instance health