AI SaaS Platform: POC to Production in 8 Weeks
A SaaS company had promising AI models stuck in Jupyter notebooks. We built the LLM, RAG, and MLOps pipeline that got them into production.
The Challenge
What was getting in the way
- 01
AI models worked well in notebooks but had no clear path to production deployment
- 02
Product releases were slow because the team kept reworking the same prototypes
- 03
AWS bills climbed 35% quarter over quarter with no infrastructure optimization in place
The Solution
How we solved it
We built an LLM + RAG + MLOps pipeline end to end. That meant connecting OpenAI models to a custom RAG layer backed by Pinecone, setting up automated retraining and deployment with MLflow, and running everything on EKS with right-sized instances. The team went from re-running notebooks to pushing model updates through a CI/CD pipeline.
Technologies
What We Built
A look inside the project
The Process
Step-by-step delivery
Legacy POC
Audit existing AI experiments and data pipelines
LLM Integration
Integrate and fine-tune LLMs for product use cases
RAG Pipeline
Build retrieval-augmented generation with vector DB
MLOps Setup
Automate model training, testing, and deployment
Production Deploy
Ship, monitor, and scale in production
The Results
The numbers
POC to Production Deployment
Cloud Cost Reduction
AI-Powered Features in Production