AI research and solutions organization
ISRAN
Enterprise RAG pipelines, domain-driven AI services, and MLOps workflows for private knowledge retrieval.
Overview
Context
ISRAN needed private-data LLM systems that could replace manual document search while fitting into a multi-team engineering environment.
Challenge
Problem Space
Knowledge retrieval was slow and engineering teams were blocked by cross-team integration bottlenecks around data-intensive AI workloads.
Solution Architecture
What shipped
Built enterprise RAG pipelines over private datasets using LangChain, Ollama-based LLMs, CrewAI orchestration, Qdrant, and HyDE-style embeddings.
Led architecture decisions across 3 engineering teams by designing domain-driven microservices for independently deployable AI services.
Implemented MLflow and Apache Airflow lifecycle patterns for training, deployment, monitoring, and rollback.
Reduced internal knowledge retrieval turnaround.
Organization-wide private knowledge access.
Independently deployable service architecture.
Technology Stack