# Dgraph Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/dgraph
> Markdown URL: https://aitinkerers.org/technologies/dgraph.md
> Technology record last updated: 2026-03-11T16:51:40Z
> Generated: 2026-08-25T07:17:00Z

Dgraph is a high-performance, distributed graph database built for production-level GraphQL and low-latency applications.

Dgraph delivers a native GraphQL experience by executing queries directly on the database engine. Built in Go, it scales horizontally across commodity hardware to handle petabytes of data while maintaining sub-second response times. The system uses a unique DQL (Dgraph Query Language) for complex joins and provides ACID transactions out of the box. Teams like Intuit and Zeotap use Dgraph to eliminate the overhead of mapping relational tables to graph structures, simplifying their backend stacks into a single, unified data layer.

- Official technology site: https://dgraph.io
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [GraphRAG in action: solving real-life use cases with intelligent retrieval and generation](https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4)

RAG serves as a prime example of a practical implementation of Generative AI, with clear ROI and proven use cases widely documented online. However, when you dive deeper and attempt to solve real-world problems, the process is often more complex than it initially appears. Off-the-shelf solutions frequently fall short, and in this talk, I’ll explain why that’s the case. As a more robust alternative, I’ll introduce a powerful approach to intelligent RAG: the GraphRAG framework, applied specifically to customer service tickets. This presents an intriguing challenge because, as in real-life scenarios, we lack a well-structured knowledge base. Instead, we must build one from existing email correspondence and resolved tickets. Traditional methods for managing such documents often struggle to maintain relevance and deliver contextually accurate answers. In this session, we’ll explore how GraphRAG—a hybrid approach that combines graph-based retrieval and generative models—can enhance customer service workflows. Through a hands-on demonstration, I’ll highlight where GraphRAG excels, its limitations, and the practical lessons learned. Ultimately, I aim to answer a question I once had: Is GraphRAG truly worthwhile, and what is its tangible impact on real-world use cases, beyond carefully curated examples?

- Event context: AI Tinkerers Poland - Second Meetup in Warsaw (January) — 2025-01-30 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4

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- [Neo4j](https://aitinkerers.org/technologies/neo4j) ([Markdown](https://aitinkerers.org/technologies/neo4j.md)) — 29 public demos
- [RAG](https://aitinkerers.org/technologies/rag) ([Markdown](https://aitinkerers.org/technologies/rag.md)) — 146 public demos
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