Case study · Data · AI
MeghGen
Agentic RAG systems for data engineering, with cloud-native pipelines that turn raw data into query-ready knowledge.
- Industry
- Data · AI
- Relationship
- Consulting engagement
- Location
- Remote
Overview
For MeghGen we built agentic RAG systems for data engineering — pipelines that take raw data and make it queryable through AI.
Challenge
Raw data is rarely ready for AI. Getting reliable answers means ingesting, cleaning and structuring it first — and keeping that pipeline observable when it runs in production.
Approach
- 01
Cloud-native pipelines that turn raw data into query-ready knowledge.
- 02
Agentic retrieval that plans how to answer a question, not just which chunks to fetch.
- 03
Reliability and observability treated as requirements, not afterthoughts.
Architecture
- Raw data sources
- Cloud pipelines
- Processing
- Indexing
- Agentic RAG
- LLM workflows
- Monitoring & observability
High-level view of the system's main layers.
What we built
- Agentic RAG systems for data engineering
- Cloud-native data pipelines
- Observability across the pipeline
Technology
- Agentic RAG
- LLM workflows
- Data pipelines
- Cloud
Outcome
A detailed write-up of results is being prepared. We're happy to walk you through the project on a call.
Screenshots
Have a product like this to build?
Tell us what you're working on. We'll tell you honestly how we'd approach it.
Prefer email? info@vertexlab.com.np