AI contract intelligence on AWS Fargate, Lambda, and multi-tenant delivery in six weeks.

THE ENGAGEMENT

A clear scope of responsibility.

Engagement

6 weeks, infrastructure delivery

Team

Cloud architect, Platform engineer, DevOps lead

Focus areas

Application infra, AI/ML deployment, multi-tenancy, CI/CD

01 / THE STARTING POINT

Situation

Fundlife.ai is building an AI-powered contract intelligence platform that helps fund administrators draft, review, and analyse legal documents. The product team (Tribes Agency) had built the application — a Next.js frontend and Django backend — but needed AWS infrastructure to get it into production.

02 / THE WORK

What we did.

• 01

  Deployed the application stack

  Stood up an ECS Fargate cluster in eu-west-1 with separate services for the Next.js frontend and Django backend, RDS PostgreSQL for the database, ECR repositories for container images, and ALBs with ACM certificates handling HTTPS. Configured custom domains (app.dev.fundlife.ai, api.dev.fundlife.ai) and resolved environment-specific issues including frontend build config and CORS settings.

• 02

  Built the AI/ML endpoint layer

  Refactored the data science team’s Python notebooks into deployable Lambda functions and stood up API Gateway at ai.dev.fundlife.ai with four endpoints: text generation (clause improvement and recommendations), conflict detection, entity recognition, and investment thesis extraction. Configured OpenAI API key management and S3 integration for tenant-specific training data.

• 03

  Enabled multi-tenant architecture

  Configured wildcard subdomain routing so each tenant gets a dedicated subdomain ({tenant}.api.dev.fundlife.ai) that maps to the same backend service, which uses the subdomain to route requests to the correct database schema. Created per-tenant S3 buckets (fundlife-{tenant}) for document and AI model data isolation.

• 04

  Wired CI/CD from GitHub

  Built GitHub Actions pipelines that auto-build Docker images and push to ECR on merge to the dev branch. ECS services pick up the new image tag and roll out automatically — zero-touch deployments for the product team.

Technologies & architecture (at a glance)

AWS ECS Fargate, Application Load Balancers, Amazon ECR, Amazon RDS (PostgreSQL), AWS Lambda, Amazon API Gateway, Amazon S3, AWS Certificate Manager, Amazon Route 53, GitHub Actions CI/CD — plus OpenAI and T5-based inference on serverless compute.

What the team received

• ECS Fargate application cluster — frontend and backend services on Fargate with ALBs, ACM certs, and custom domains in eu-west-1.
• RDS PostgreSQL database — managed database instance with tenant-isolated schemas and automated backups.
• AI Lambda endpoints — four Lambda functions behind API Gateway at ai.dev.fundlife.ai: text generation, conflict detection, entity recognition, thesis extraction.
• Multi-tenant infrastructure — wildcard subdomain routing, per-tenant S3 buckets, and schema-level data isolation.
• CI/CD pipeline — GitHub Actions to ECR to ECS auto-deploy on merge to dev branch.
• Domain & certificate management — fundlife.ai domain migrated to Route 53 with ACM certificates for all subdomains.

03 / THE DELIVERY

Timeline

Six weeks from initial deploy to full production stack including AI endpoints. Scope expanded during the engagement as the product team added AI features and multi-tenant requirements.

• Week 1

  Initial application deploy

  ECS cluster, ECR repos, RDS PostgreSQL, and ALB provisioned. Frontend and backend services live on temporary domains ahead of custom domain cutover.

• Weeks 2–3

  CI/CD, domains & environment config

  GitHub Actions pipelines wired, fundlife.ai domain migrated, custom subdomains configured, CORS and environment variable issues resolved.

• Weeks 4–5

  AI/ML layer & multi-tenancy

  Lambda functions deployed behind API Gateway, notebook code refactored, wildcard subdomain routing and per-tenant S3 buckets configured.

• Week 6

  Testing, hardening & handoff

  End-to-end testing across all endpoints, security review, and operational handoff to the product team.

04 / THE OUTCOME

Impact & ROI

Fundlife.ai went from application code on localhost to a production platform they could demo to clients in six weeks. Without StationOps, the product team would have needed to hire a DevOps engineer, learn AWS, and piece together the infrastructure themselves — burning months before a single client could see the product. The comparison below shows what this would typically look like as an internal effort.

Figures shown are typical ranges for comparable work and will vary by baseline maturity, constraints, and team size.

• Product team unblocked — Tribes Agency could develop and demo features against real infrastructure instead of localhost — enabling the first client demo within weeks.
• AI features in production — four AI endpoints live and callable from the frontend, with the T5 model and OpenAI integration running on serverless compute.
• Tenant-ready from day one — multi-tenant data isolation baked into the architecture: new tenants onboarded by creating a subdomain and S3 bucket.
• Zero-touch deployments — product engineers merge to dev and changes go live automatically across the full stack.

Measured outcomes.

• Production platform for client demos in 6 weeks
• 4 AI Lambda endpoints behind API Gateway
• Multi-tenant subdomains and per-tenant S3 isolation
• GitHub → ECR → ECS automated deploys

“StationOps took our application code and gave us a production platform we could demo to clients. The AI endpoints, multi-tenancy, and CI/CD pipeline all just worked.”

Head of Product, Tribes Agency (Fundlife.ai)