Duplicati began as an open source backup tool. It was built to provide a simple, privacy-first way to encrypt and store data anywhere.

For more than a decade, individuals, developers, and organizations have trusted Duplicati to protect their files. That mission still matters. Reliable backup and strong encryption remain the foundation of the project and will continue to be supported.

At the same time, the role of data inside organizations is changing.

Backup systems were originally designed for disaster recovery. Their purpose was simple. Protect information and restore it if something fails. For many years that model worked well, but the rise of AI is changing how companies think about data and how they use the information they already store.

Every enterprise now accumulates enormous volumes of historical data. Emails, logs, research documents, transaction records, code repositories, and model outputs build up over time. Much of this information eventually ends up in backup systems.

Traditionally these archives are treated as cold storage. They sit quietly in the background and are only accessed when something breaks or data must be recovered. Yet those same archives contain a detailed record of how an organization operates. They capture operational history, decisions, experiments, and patterns that unfold over years of work.

In many cases this historical record becomes one of the most complete datasets a company possesses.

The reason it remains underused is structural. Most organizations run two separate data environments. One environment consists of operational platforms such as Snowflake, Databricks, kdb+, or internal data lakes. These systems support analytics, model development, and experimentation. The other environment consists of backup platforms that exist to preserve historical data for compliance and recovery.

One system is designed for analysis while the other preserves long term history. Because these environments rarely intersect, the historical layer of data stored in backups is difficult to access or incorporate into modern workflows. As a result, companies often invest heavily in external datasets and new pipelines while their own archives remain largely untouched.

We believe this separation no longer makes sense.

Backups should do more than preserve information. They should also help organizations learn from their own history.

This idea is shaping the next phase of Duplicati.

Duplicati is evolving into AI native training data infrastructure. Instead of storing encrypted archives that remain untouched, Duplicati will help structure historical data so it can be used in modern AI workflows.

The goal is not to replace existing analytics platforms or data lakes. Those systems remain essential. The goal is to unlock the historical layer of data that already exists inside an organization and make it usable. Over time this means turning backup archives into systems that support searchable knowledge, vectorized datasets, model training pipelines, and internal AI copilots built on proprietary data, all while maintaining the security and compliance guarantees that backup infrastructure requires.

This vision builds on a natural starting point. Duplicati already operates in a unique position within enterprise infrastructure. It touches large volumes of historical data, works across many storage backends, encrypts sensitive information, and manages long term data retention. In practical terms, Duplicati already manages the memory of an organization. The next step is making that memory accessible and useful.

While this means that the commercial direction of Duplicati Inc is evolving, we are still an open-core company and committed to supporting the Duplicati open-source project and the core backup functionality. But we are building a company focused on enterprise data infrastructure and AI systems. This reflects where we believe our technology can create the most value.

As part of this shift we will also change the way we onboard users into our cloud offerings. Going forward we will focus more on organizations building large scale data platforms and internal AI capabilities.

The long term goal is straightforward. We want to help organizations turn their historical data into usable intelligence. When a company can access and learn from its own history, it becomes easier to build internal AI systems that reflect how that organization actually operates.

For decades backups were treated as insurance policies that existed to restore systems after failure. In the age of AI they can become something far more valuable. Backups hold the institutional memory of an enterprise, and the organizations that learn how to unlock that memory will build the next generation of intelligent systems.

Duplicati aims to help make that possible.