Data infrastructure for the agentic era

For a decade, the primary goal of data engineering was to populate a dashboard for a human executive. This traditional Business Intelligence (BI) paradigm assumed a human was the final consumer of data. The rise of autonomous AI agents changes this requirement. An agent cannot wait for a nightly batch job or navigate a complex UI. It needs a high-performance data foundation that allows it to query its own history and current environment in real time. This is the gap fiveonefour aims to fill.

Founded in 2023, fiveonefour is building a developer platform designed to embed analytics directly into the AI stack. Their primary vehicle is MooseStack, an open-source framework that abstracts the complexity of building on top of ClickHouse. ClickHouse has emerged as a preferred engine for real-time analytical workloads due to its columnar storage and speed, but it remains difficult to manage and integrate into modern application workflows. MooseStack simplifies this by providing an interface that treats data infrastructure as code, making it accessible to application developers rather than just specialized data engineers.

The MooseStack framework

The company's approach centers on the idea of "agent-friendly" data. In a standard Retrieval-Augmented Generation (RAG) setup, a Large Language Model (LLM) retrieves static documents. In an agentic setup, the agent often needs to know what just happened—user clickstreams, system logs, or transaction histories—to decide on its next action. Traditional data warehouses are too slow for this latency requirement, and transactional databases are too inefficient for complex aggregation. By leveraging the speed of ClickHouse and the integration layer provided by MooseStack, fiveonefour allows developers to give agents a high-speed memory of real-time events.

Based in Montreal, fiveonefour occupies a specific niche between the database layer and the application layer. They are not building a new database engine from scratch; they are building the connective tissue that makes existing high-performance databases accessible to the next generation of software. This strategy avoids direct competition in the "database wars" and instead focuses on the developer experience of building data-intensive applications.

Market position and validation

The company's $17 million Series A investment, led by Dimension Capital, suggests a growing appetite for specialized data infrastructure for AI. As companies move beyond simple chatbots and toward agents that can execute complex tasks, the need for low-latency feedback loops becomes critical. Competitive pressures come from established data platforms like Snowflake or Databricks, which are rushing to add real-time and AI features, as well as managed ClickHouse providers like Tinybird. However, fiveonefour’s focus on an open-source framework provides a distinct path for developers who want to avoid vendor lock-in while maintaining the performance of a dedicated analytical engine. They are betting that as agents become the primary users of software, the underlying data stack must be rebuilt for their specific speed and access patterns.