# Blazegraph Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/blazegraph
> Markdown URL: https://aitinkerers.org/technologies/blazegraph.md
> Technology record last updated: 2026-03-11T16:51:43Z
> Generated: 2026-09-23T09:41:04Z

Blazegraph is a high-performance graph database optimized for RDF/SPARQL and Blueprints APIs, capable of scaling to 50 billion triples on a single machine.

Blazegraph (formerly Bigdata) provides a robust, ultra-scalable platform for graph-based applications. It supports the W3C SPARQL 1.1 standard and offers a high-concurrency architecture that powers mission-critical deployments like the Wikidata Query Service. The platform features an N-Way High Availability (HA) mode for consistent performance and a GPU-accelerated analytics engine (Blazegraph DASL) that executes complex graph traversals up to 100x faster than traditional CPU-based methods. Its ability to handle massive datasets with low latency makes it a primary choice for enterprise knowledge graphs and linked data projects.

- Official technology site: https://blazegraph.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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