ISRAN
Enterprise RAG pipelines, domain-driven AI services, and MLOps workflows for private knowledge retrieval.
AI research and solutions organization
System overview
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Continue to project detailsPrivate datasets feed LangChain retrieval with HyDE-style embeddings and Qdrant. Retrieved context supports CrewAI orchestration and Ollama language models for private knowledge access. Retrieval turnaround improved by 35% for an organization of 200+ people. MLflow and Airflow support the model lifecycle.
Context
ISRAN needed private-data LLM systems that could replace manual document search while fitting into a multi-team engineering environment.
Problem space
Knowledge retrieval was slow and engineering teams were blocked by cross-team integration bottlenecks around data-intensive AI workloads.
Solution architecture
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.
- Faster retrieval
- 35%
Reduced internal knowledge retrieval turnaround.
- People served
- 200+
Organization-wide private knowledge access.
- AI services
- 5+
Independently deployable service architecture.
Technology stack
- LangChain
- Ollama
- CrewAI
- Qdrant
- HyDE
- MLflow
- Airflow
- Microservices