Flintrock Fund I has backed eight foundational infrastructure companies that collectively represent the AI application layer's data and ML substrate.
Every company occupies a distinct load-bearing position in the AI application stack.
An embeddable analytical query engine that runs complex SQL on local or remote data without a server process. Powers the next generation of data-intensive applications and notebooks.
High-performance vector similarity search engine designed for AI applications. Handles billion-scale embedding retrieval with built-in filtering and payload indexing.
Open-source framework for building, shipping, and scaling ML-powered services. Abstracts infrastructure complexity from model serving across cloud and on-premise environments.
Open-source AI-native database that stores objects and vectors simultaneously. Combines vector search with structured filtering for contextual AI retrieval at scale.
Python-native workflow orchestration platform for data and ML pipelines. Observable, recoverable, and developer-first — built to handle real production failure modes.
GraphQL data gateway that federates data sources at the edge. Enables AI applications to query multiple backends through a unified, performant schema layer.
Feature platform for real-time ML models. Lets teams define features in Python and serves them at millisecond latency — closing the gap between model development and production.
Indexing engine for unstructured data that updates incrementally rather than rebuilding from scratch. Critical infrastructure for AI systems that operate over live, evolving document corpora.
Portfolio reflects investments made by Flintrock Fund I as of the date of this publication. Stage and year reflect Flintrock's entry point. Past performance is not indicative of future results.