# ArangoDB Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/arangodb
> Markdown URL: https://aitinkerers.org/technologies/arangodb.md
> Technology record last updated: 2026-03-11T16:51:37Z
> Generated: 2026-09-21T10:50:34Z

A native multi-model database engine that unifies documents, graphs, and key-values in a single C++ core using one query language (AQL).

ArangoDB eliminates complexity by handling JSON documents, complex graph relations, and key-value pairs within a single database instance. Its native multi-model architecture ensures ACID compliance and high performance without the data redundancy of polyglot persistence. Developers use ArangoDB Query Language (AQL) to join data across different models in one statement, reducing network overhead. With features like the SmartGraph module for sharding large datasets and the integrated ArangoSearch engine for full-text indexing, it scales horizontally to meet enterprise-grade demands.

- Official technology site: https://www.arangodb.com
- Public AI Tinkerers demos and talks: 2
- 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

### [CodeDD - AI based code due diligence](https://vienna.aitinkerers.org/talks/rsvp_fPek6RNCqtM)

A tool for AI (LLMs) based code review for startups and investors alike. Easy: Copy git repo Complete: +40 code quality parameters Secure: No line of code is disclosed or stored

- Event context: AI Tinkerers Vienna ICML Edition — 2024-07-24 — Vienna
- Public talk page: https://vienna.aitinkerers.org/talks/rsvp_fPek6RNCqtM

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